Abstract: Described is a system for converting convolutional neural networks to spiking neural networks. A convolutional neural network (CNN) is adapted to fit a set of requirements of a spiking neural network (SNN), resulting in an adapted CNN. The adapted CNN is trained to obtain a set of learned weights, and the set of learned weights is then applied to a converted SNN having an architecture similar to the adapted CNN. The converted SNN is then implemented on neuromorphic hardware, resulting in reduced power consumption.

Abstract: Disclosed herein is a technique for implementing a framework that enables application developers to enhance their applications with dynamic adjustment capabilities. Specifically, the framework, when utilized by an application on a mobile computing device that implements the framework, can enable the application to establish predictive models that can be used to identify meaningful behavioral patterns of an individual who uses the application. In turn, the predictive models can be used to preempt the individual's actions and provide an enhanced overall user experience. The framework is configured to interface with other software entities on the mobile computing device that conduct various analyses to identify appropriate times for the application to manage and update its predictive models. Such appropriate times can include, for example, identified periods of time where the individual is not operating the mobile computing device, as well as recognized conditions where power consumption is not a concern.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for organizing trained and untrained neural networks. In one aspect, a neural network device includes a collection of node assemblies interconnected by between-assembly links, each node assembly itself comprising a network of nodes interconnected by a plurality of within-assembly links, wherein each of the between-assembly links and the within-assembly links have an associated weight, each weight embodying a strength of connection between the nodes joined by the associated link, the nodes within each assembly being more likely to be connected to other nodes within that assembly than to be connected to nodes within others of the node assemblies.

Abstract: A fault detection and diagnosis (FDD) system designed to automatically and efficiently determine the true fault underlying a set of symptoms, presenting the operator with a clear cause and effect diagram for all faults and symptoms. An Event Cluster is used to group related symptoms and causes, potentially across multiple pieces of equipment, and establish a cause and effect relationship chain.

Abstract: Dimensionality reduction systems and methods facilitate visualization, understanding, and interpretation of high-dimensionality data sets, so long as the essential information of the data set is preserved during the dimensionality reduction process. In some of the disclosed embodiments, dimensionality reduction is accomplished using clustering, evolutionary computation of low-dimensionality coordinates for cluster kernels, particle swarm optimization of kernel positions, and training of neural networks based on the kernel mapping. The fitness function chosen for the evolutionary computation and particle swarm optimization is designed to preserve kernel distances and any other information deemed useful to the current application of the disclosed techniques, such as linear correlation with a variable that is to be predicted from future measurements. Various error measures are suitable and can be used.

Abstract: An apparatus for use within a cognitive information processing system comprising: an insight/learning engine, the insight/learning engine encapsulating an operation, the operation being applied to a target cognitive graph to generate a cognitive insight.

Abstract: A method, system and computer-usable medium for performing cognitive computing operations comprising receiving streams of data from a plurality of data sources; processing the streams of data from the plurality of data sources, the processing the streams of data from the plurality of data sources performing data enriching for incorporation into a cognitive graph; defining a cognitive persona within the cognitive graph, the cognitive persona corresponding to an archetype user model, the cognitive persona comprising a set of nodes in the cognitive graph; associating a user with the cognitive persona; and, performing a cognitive computing operation based upon the cognitive persona associated with the user.

Abstract: According to various embodiments, a ratings matrix including matrix values is generated, each row of the ratings matrix identifying one of a plurality of users, each column of the ratings matrix identifying one of a plurality of items, and each of the matrix values corresponding to a known affinity rating describing a degree of affinity associated with one of the users and one of the items. The ratings matrix may include a missing entry representing an unknown affinity rating. According to various embodiments, a revised ratings matrix is generated by factoring the ratings matrix into a user matrix and an item matrix, the revised ratings matrix being the product of the user matrix and the item matrix and including at least one entry representing a predicted affinity rating in place of the missing entry.

Abstract: Systems and methods are disclosed for monitoring operation of an automation system that includes a plurality of interconnected logical objects. The systems and methods may build a fuzzy cognitive map to model an interdependence of the plurality of interconnected logical objects upon one another. In some examples, the systems and methods may identify a non-ideality associated with at least one of the plurality of logical objects and determine from the fuzzy cognitive map an effect of the non-ideality on the operation of the automation system. In some examples, the systems and methods may determine from the fuzzy cognitive map a first one of the plurality of logical objects that affects a second one of the plurality of logical objects to a greater extent than do the remaining ones of the plurality of logical objects.

Abstract: Embodiments are disclosed for data computation of collaborative filtering in a social network. Collaborative filtering involves predicting a user's behavior or interests based on other users' behavior or interests. To predict a user's interests in an item such as a picture, a system performs an iterative computation to perform an evaluation by solving an objective function. The system characterizes “users” as “vertices” in a directed graph, “relationship among users” as “edges” in the directed graph, and “items” as “worker data” that is locally-calculated, stored, and managed in individual worker computers. When a local computing process is completed, the “worker data” can be transferred to other worker computers so as to complete a whole computing process. The system enhances an overall computing efficiency and enables collaborative filtering across a large data set.

Abstract: A machine learning apparatus includes a state observing unit and a learning unit. The state observation unit observes, during repeated processing operations, a state variable including a processing downtime and at least one of an actual one-cycle processing time of a processing machine and the temperature of a motor. The learning unit learns a value for selecting, based on the state variable and an overheat alarm level, the shortest processing downtime, which satisfies the fact that the temperature of the motor does not exceed the overheat alarm level when the repeated processing operations are performed.

Abstract: Systems and methods to create content for interactive synthetic characters are provided. In some embodiments, a conversation editor may be configured to create a traversable script for an interactive synthetic character by receiving conversation rules from a user. These rules can be used to match words or phrases that a user speaks or types, or to monitor for a physical movement of the user or synthetic character. Each rule can include responses to be performed by the interactive synthetic character. Examples of responses include producing audible or textual speech for the synthetic character, performing animations, playing sound effects, retrieving data, and the like. A traversable script can be generated from the conversation rules that when executed by the synthetic character allows for the dynamic interactions. In some embodiments, the traversable script can be navigated by a state engine using navigational directives associated with the conversation rules.

Abstract: One embodiment of the invention provides a system for mapping a neural network onto a neurosynaptic substrate. The system comprises a reordering unit for reordering at least one dimension of an adjacency matrix representation of the neural network. The system further comprises a mapping unit for selecting a mapping method suitable for mapping at least one portion of the matrix representation onto the substrate, and mapping the at least one portion of the matrix representation onto the substrate utilizing the mapping method selected. The system further comprises a refinement unit for receiving user input regarding at least one criterion relating to accuracy or resource utilization of the substrate. The system further comprises an evaluating unit for evaluating each mapped portion against each criterion. Each mapped portion that fails to satisfy a criterion may be remapped to allow trades offs between accuracy and resource utilization of the substrate.

Abstract: A system for selecting an optimal data set from a plurality of candidate data sets based at least in part on a client data packet received through a web interface may include a web server that receives first data packets from a client device, and a cloud computing platform that receives the first data packets from the web server and validates the first data packets using information imported from a third-party computer system. The platform may also receive second data packets from a second third-party computer system, select candidate data sets from a collection of available data sets based on information that is descriptive of the user, and select at least one optimal data set from the candidate data sets based on a user priority. The optimal data set may include adjustable parameters with values that are set through the client device.

Abstract: Methods and systems are described for determining occupancy with user provided information. According to at least one embodiment, a method for determining occupancy with user provided information includes using at least one sensor to detect occupancy in a building over time, determining a predictive schedule based on the occupancy detected with the at least one sensor, and requesting information relevant to the predictive schedule from a user.

Abstract: A governance apparatus and a communication method for communicating within the governance apparatus. The governance apparatus includes a Government. The Government includes Councils such that a macro grid including an artificial intelligence and the Government is configured to respond to an alert pertaining to an event through use of the artificial intelligence and the Government. The governance apparatus also includes an enhanced Transmission Control Protocol/Internet Protocol (TCP/IP) communication stack of layers including a Governance Layer and an Intelligence Layer. The Intelligence Layer includes intelligence software configured to process data pertaining to the event, data pertaining to the alert, and data pertaining to the Government.

Abstract: A system for guiding and evaluating physical positioning, orientation and motion of the human body, comprising: a cloud computing-based subsystem including an artificial neural network and spatial position analyzer said cloud computing-based subsystem adapted for data storage, management and analysis; at least one motion sensing device wearable on the human body, said at least one motion sensing device adapted to detect changes in at least one of spatial position, orientation, and rate of motion; a mobile subsystem running an application program (app) that controls said at least one motion sensing device, said mobile subsystem adapted to capture activity data quantifying said changes in at least one of spatial position, orientation, and rate of motion, said mobile subsystem further adapted to transfer said activity data to said cloud computing-based subsystem, wherein said cloud computing-based subsystem processes, stores, and analyzes said activity data.

Abstract: A method and system for teaching an object of a deictic reference to a machine. A processor of the machine teaches the object of the deictic reference to the machine which results in the machine learning the object. The teaching includes: the processor finds an item in a region indicated by a physical pointing gesture, by the user, that points to the object; the processor shines a laser light on the item and in response, the processor receives a negative spoken indication from the user that the item shined on by the laser light is not the object; in response to the negative spoken indication from the user, the processor interacts with the user in an iterative procedure wherein the machine learns the object in a final iteration of the procedure. The processor stores the learned object in a storage repository.

Abstract: A device, method and system for automatically inferring a mobile user's current context includes applying a user activity knowledge base to real-time inputs and stored user-specific information to determine a current situation. Automated reasoning is used to infer a user-specific context of the current situation. Automated candidate actions may be generated and performed in accordance with the current situation and user-specific context.