Content attribution system for artificial intelligence and machine learning systems

A computer implemented method of tracking updates to a neural network, the method comprising:

• using a computer connected to a data communications network to access multiple snapshots of the neural network and a current version of the neural network;
• wherein the respective snapshots comprise weights of respective inputs in the neural network;
• retrieving identification tags from a computer memory file, wherein the identification tags correspond to training data sets used to configure the respective inputs within the neural network prior to the respective snapshots;
• analyzing outputs of the current version of the neural network by performing computer-implemented steps with a processor, the computer implemented steps further comprising:
• applying a stimulus data set to the current version of the neural network to retrieve an output of the neural network;
• identifying active nodes used to propagate the output from the current version of the neural network;
• for the active nodes of the current version, calculating differential values of trained weights relative to respective snapshots from the multiple snapshots;
• associating the identification tags of the training data sets with the differential values.

A computer implemented method of tracking updates to a neural network, the method comprising:

• using a computer connected to a data communications network to access a selected snapshot of nodes in a neural network at a first time;
• using the computer to access at least one different snapshot of the nodes in the neural network, wherein the different snapshot corresponds to a different time,
• wherein the selected snapshot and the at least one different snapshot comprise weights of respective inputs in the neural network;
• retrieving identification tags from a computer memory file, wherein the identification tags correspond to training data sets used to configure the respective inputs and the weights within the neural network prior to the respective snapshots;
• maintaining a current version of the neural network by performing computer-implemented steps with a processor, the computer implemented steps further comprising:
• using the selected snapshot, identifying trained weights of the respective inputs in the neural network;
• using the at least one different snapshot, identifying different trained weights of the respective inputs in the neural network;
• iteratively incorporating the respective inputs, according to the different trained weights for each different snapshot, into the current version of the neural network;
• calculating input weight differential values for each iteration of incorporating the respective inputs of different trained weights into the current version of the neural network;
• and
• associating the identification tags of the training data sets with the differential values and storing the differential values for each iteration and the associated identification tags in the computer memory;
• editing the weights of the neural network to account for removing contributions of at least one of the training data sets to the iterations incorporating respective inputs into the neural network.

A computer implemented method of tracking updates to a neural network, the method comprising:

• using a computer connected to a data communications network to access a selected snapshot of nodes in a neural network at a first time;
• using the computer to access at least one different snapshot of the nodes in the neural network, wherein the different snapshot corresponds to a different time,
• wherein the selected snapshot and the at least one different snapshot comprise weights of respective inputs in the neural network;
• retrieving identification tags from a computer memory file, wherein the identification tags correspond to training data sets used to configure the respective inputs and the weights within the neural network prior to the respective snapshots;
• maintaining a current version of the neural network by performing computer-implemented steps with a processor, the computer implemented steps further comprising:
• using the selected snapshot, identifying trained weights of the respective inputs in the neural network;
• using the at least one different snapshot, identifying different trained weights of the respective inputs in the neural network;
• iteratively incorporating the respective inputs, according to the different trained weights for each different snapshot, into the current version of the neural network;
• calculating input weight differential values for each iteration of incorporating the respective inputs of different trained weights into the current version of the neural network;
• and
• associating the identification tags of the training data sets with the differential values and storing the differential values for each iteration and the associated identification tags in the computer memory;
• editing the weights of the neural network to modify its function by scaling (amplify or attenuate) contributions of at least one of the training data sets to the iterations incorporating respective inputs into the neural network.