Numenta, Inc.

Vicarious Fpc, Inc.

Intrinsic Innovation LLC

Intellectual Ventures I LLC

Gallitzin Allegheny LLC

Dileep George

Jeffrey Charles Hawkins

Robert Gilchrist Jaros

Kenneth Alan Kansky

Frank Emmanuel Astier

Bhaskara Mannar Marthi

Christopher Remmert Laan

Wolfgang Lehrach

D Scott Phoenix

Jeffrey L Edwards

Subutai Ahmad

David Scott Phoenix

Miguel Lazaro-Gredilla

Eric J Purdy

Ronald Marianetti, Ii

William Cooper Saphir

David A Mely

Ronald Marianetti

Tom Silver

Austin Charles Stone

Mohamed Eldawy

Huayan Wang

Nishad Gothoskar

Wolfang Lehrach

Anosh Raj

James Niemasik

Charles Curry

Miguel Lazaro Gredilla

Yasemin Bekiroglu

Wiliam C Saphir

Abhishek Agarwal

Guangyao Zhou

J Swaroop Guntupalli

Dianhuan Lin

Antoine Dedieu

Miguel Lázaro-Gredilla

A system for event prediction using schema networks includes a first antecedent entity state that represents a first entity at a first time; a first consequent entity state that represents the first entity at a second time; a second antecedent entity state that represents a second entity at the first time; and a first schema factor that couples the first and second antecedent entity states to the first consequent entity state; wherein the first schema factor is configured to predict the first consequent entity state from the first and second antecedent entity states.

Systems and methods for event prediction using schema networks

A system and method for machine understanding, using program induction, includes a visual cognitive computer including a set of components designed to execute predetermined primitive functions. The method includes determining programs using a program induction engine that interfaces with the visual cognitive computer to discover programs using the predetermined primitive functions and/or executes the discovered programs based on an input.

Method and system for machine concept understanding

A method for generating data explanations in a recursive cortical network includes receiving a set of evidence data at child feature nodes of a first layer of the recursive cortical network, setting a transformation configuration that directs messaging of evidence data and transformed data between layers of the network, performing a series of transformations on the evidence data according to the transformation configuration, the series including at least one forward transformation and at least one reverse transformation, and outputting the transformed evidence data.

Systems and methods for generating data explanations for neural networks and related systems

A method for inferring patterns in multi-dimensional image data comprises providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; wherein each sub-network is associated with a distinct subset of the space; configuring nodes of the sub-networks with posterior distribution component; receiving image data feature input at the final child feature nodes; propagating node activation through the network layer hierarchy in a manner consistent with node connections of sub-networks of the network and the posterior prediction of child nodes; and outputting parent feature node selection to an inferred output.

System and method for a recursive cortical network

A system for teaching compositionality to convolutional neural networks includes an unmasked convolutional neural network comprising a first set of convolutional neural network layers; a first masked convolutional neural network comprising a second set of convolutional neural network layers; the unmasked convolutional neural network and the first masked convolutional network sharing convolutional neural network weights; the system training the unmasked and first masked convolutional neural networks simultaneously based on an objective function that seeks to reduce both discriminative loss and compositional loss.

System and method for teaching compositionality to convolutional neural networks

A system includes: a recursively architected network of sub-networks organized into a hierarchical layers; the sub-networks including at least a parent feature node, a pool node, a parent-specific child feature (PSCF) node, and a child feature node; the parent feature node of at least one sub-network configured with a selection function actionable on at least two pool nodes connected to the parent feature node of the at least one sub-network; the pool node of the at least one sub-network configured with a selection function actionable on at least two PSCF nodes connected to the pool node of the at least one sub-network; the PSCF node of the at least one sub-network configured to activate a connected child feature node; the child feature node connectable to at least a parent feature node of a sub-network at a lower hierarchical layer.

A method for robot control using visual feedback including determining a generative model S, training the generative model S, and controlling the robot using the trained generative model S

Method and system for robot control using visual feedback

The method for query training can include: determining a graphical representation, determining an inference network based on the graphical representation, determining a query distribution, sampling one or more train queries from the query distribution, and optionally determining a trained inference network by training the untrained inference network using the train query. The method can optionally include determining an inference query and determining an inference query result for the inference query using the trained inference network.

Method and system for query training

A web-based hierarchical temporal memory (HTM) system in which one or more client devices communicate with a remote server via a communication network. The remote server includes at least a HTM server for implementing a hierarchical temporal memory (HTM). The client devices generate input data including patterns and sequences, and send the input data to the remote server for processing. The remote server (specifically, the HTM server) performs processing in order to determine the causes of the input data, and sends the results of this processing to the client devices. The client devices need not have processing and/or storage capability for running the HTM but may nevertheless take advantage of the HTM by submitting a request to the HTM server.

Hierarchical temporal memory (HTM) system deployed as web service

A method for generating patterns with a network includes providing a recursive network of sub-networks with a parent feature node and at least two child feature nodes; and at a first sub-network of the recursive network, the first sub-network including the parent feature node and the at least two child feature nodes, selecting a first pool node and a second pool node consistent with a selection function of the parent feature node, selecting at least a first parent-specific child feature (PSCF) node that corresponds to a first child feature node of the sub-network, selecting at least a second parent-specific child feature (PSCF) node that corresponds to a second child feature node of the sub-network; and compiling the state of final child feature nodes, including the first and second child feature nodes, of the network into a generated output.

Growing community of inventors

Menlo Park, CA, United States of America

Dileep George