Recursive neural networks on future event prediction

A system for training a recursive neural network (RNN), the system comprising:

• a processor, configured to:
• generate, using the RNN, a plurality of embedding vectors based on a plurality of observations, wherein the observations include (i) a subject, (ii) an action taken by the subject, and (iii) an object on which the subject is taking the action on, wherein the subject and object are constant;
• generate based on a prediction score, predictions of one or more future events based on one or more comparisons of at least some of the plurality of embedding vectors, the RNN being trained to distinguish between likely and unlikely future events and to rank and score future events determined to be the likely events, wherein the prediction score represents a probability that an event Y will occur if an event X occurs first, a rank loss is calculated based on a determined accuracy of the predictions, and margin loss is minimized during training of the RNN;
• and
• initiate based on the predictions, an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.

A non-transitory computer-readable storage medium comprising a computer-readable program for training a recursive neural network (RNN), wherein the computer-readable program when executed on a computer causes the computer to perform the steps of:

• generating, by a processor using the RNN, a plurality of embedding vectors based on a plurality of observations, wherein the observations include (i) a subject, (ii) an action taken by the subject, and (iii) an object on which the subject is taking the action on, wherein the subject and object are constant;
• generating, by the processor, based on a prediction score, predictions of one or more future events based on one or more comparisons of at least some of the plurality of embedding vectors, the RNN being trained to distinguish between likely and unlikely future events and to rank and score future events determined to be the likely events, wherein the prediction score represents a probability that an event Y will occur if an event X occurs first, a rank loss is calculated based on a determined accuracy of the predictions, and margin loss is minimized during training of the RNN;
• and
• initiating, by the processor, based on the predictions, an action to a hardware device to mitigate expected harm to at least one item selected from the group consisting of the hardware device, another hardware device related to the hardware device, and a person related to the hardware device.