Ptc Inc.

Aqeel, LLC

Bruce Katz

Joseph John Pizonka

Ryan Todd Caplan

Allon Guez

Jon Guez

Max Peysakhov

A method for predicting variables of interest related to a system includes collecting one or more sensor streams over a time period from sensors in the system and generating one or more anomaly streams for the time period based on the sensor streams. Values for variables of interest for the time period are determined based on the sensor streams and the anomaly streams. Next, a time-series predictive algorithm is applied to the (i) the sensor streams, (ii) the anomaly streams, and (iii) the values for the variables of interest to generate a model for predicting new values for the variables of interest. The model may then be used to predict values for the variables of interest at a time within a new time period based on one or more new sensor streams.

Detection and use of anomalies in an industrial environment

A closed loop brain stimulation system includes a means for reading electrophysiological signals from the brain; means for reading other biometric signals; means for stimulating the brain; and a control algorithm that dynamically adjusts the parameters of stimulation to minimize a reference utility function that computes the difference between the current and desired brain states, and current and desired biometric states.

Method and apparatus for closed-loop brain stimulation

A computer-implemented method of automating inductive bias selection includes a computer receiving a plurality of examples, each example providing a plurality of feature-value pairs. The computer constructs an inductive bias dataset which correlates each respective example in the plurality of examples with numerical indications of training quality. The numerical indications of training quality for each respective example are generated by creating a plurality of models, with each model corresponding to a distinct set of inductive biases. The training quality for each respective model is evaluated when applied to the respective example. The computer uses the inductive bias dataset to select a plurality of inductive biases for application to one or more new datasets.

Automated methodology for inductive bias selection and adaptive ensemble choice to optimize predictive power

A computer-implemented method for scoring an example with causal information includes a computer system receiving an indication of a goal and applying a predictive model to the example to yield an output score with respect to the goal. The computer system produces causal score for each feature-value pair in the example, each causal score indicating of a relative extent to which the feature-value pair is responsible for influencing the output score. Then, the computer system presents the output score and the causal score for each feature-value pair in the example.

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Philadelphia, PA, United States of America

Bruce Katz