Abstract: Disclosed is a system for recording and storing sequences of operations that may be performed on a client device. During a training phase, the system collects information about operations performed by a user or by an automatic application crawling program. Information about operations may include view-structure data about components of a user interface and event data about interactions the user has with the interface components and the outcome of such interactions. The operation data is stored in an operation graph that may represent how specific user interactions with interface components can cause transitions to new view-structures of the same or different applications on the client device. The data stored in the operation graph may be used to construct new sequences of operations for later execution, for example, in response to a user query.

Abstract: Disclosed is a data processing system that includes compile time logic configured to section a graph into a sequence of sections, and configure each section of the sequence of sections such that an input layer of a section processes an input, one or more intermediate layers of the corresponding section processes corresponding one or more intermediate outputs, and a final layer of the corresponding section generates a final output. The final output has a non-overlapping final tiling configuration, the one or more intermediate outputs have corresponding one or more overlapping intermediate tiling configurations, and the input has an overlapping input tiling configuration. The compile time logic is further to determine the various tiling configurations by starting from the final layer and reverse traversing through the one or more intermediate layers, and ending with the input layer.

Abstract: Systems, methods, and computer readable media related to information retrieval. Some implementations are related to training and/or using a relevance model for information retrieval. The relevance model includes an input neural network model and a subsequent content neural network model. The input neural network model and the subsequent content neural network model can be separate, but trained and/or used cooperatively. The input neural network model and the subsequent content neural network model can be “separate” in that separate inputs are applied to the neural network models, and each of the neural network models is used to generate its own feature vector based on its applied input. A comparison of the feature vectors generated based on the separate network models can then be performed, where the comparison indicates relevance of the input applied to the input neural network model to the separate input applied to the subsequent content neural network model.

Abstract: At least a subset of first processing units of a first arrangement of an array of processing units is assigned to perform computations of a first layer of a neural network and at least a subset of second processing units of a last arrangement is assigned to perform computations of a second layer. At least a second subset of the first processing units of the first arrangement is re-assigned to perform computations of a third layer. In one aspect, it is determined that a number of layers exceeds a number of arrangements of processing units of a systolic processing chip. A first arrangement of processing units of the number of arrangements is assigned to perform computations according to a first layer for a first set of forward propagations, and the first arrangement is assigned to perform computations according to a different layer for a second set of forward propagations.

Abstract: An information processing device according to one aspect of the present invention includes a first acquirer configured to acquire measured data of a sensor, a second acquirer configured to acquire maintenance information related to maintenance performed on the sensor, a learner configured to learn teacher data in which the acquired maintenance information as label information is associated with the acquired measured data to generate a determination model, and a storage storing the generated determination model.

Abstract: A method of machine learning that teaches a computer to determine likelihood that a medical journal article is classified as high value for an intended system. In some embodiments, the method includes procuring, in a medical articles database on the computer, a training set including medical articles recommended by subject matter experts associated with medical journals that have published the medical articles. The method can also include identifying, by a feature extraction controller of the computer, first features in the medical articles via a remote annotation service and a remote article information service. The method can also include identifying, by intended system services electronically available to the intended system, second features in the medical articles. The method can also include reducing, by the feature extraction controller, the first and second features to form a set of relevant features.

Abstract: A neuromorphic device may include: a pre-synaptic neuron; a synapse electrically connected with the pre-synaptic neuron through a row line; and a post-synaptic neuron electrically connected with the synapse through a column line. The post-synaptic neuron may include a first inverter, the first inverter comprising a first pull-up transistor and a first pull-down transistor, a body of the first pull-up transistor and a body of the first pull-down transistor being electrically connected with a first output node of the first inverter.

Abstract: For confidence weighting relationships between complex entities in unstructured data an expressed relationship between a subset of a set of tokens is extracted from a knowledge graph corresponding to the unstructured data. The knowledge graph includes the set of tokens. The tokens in the subset of tokens are related in the expressed relationship by a set of predicates. A number of occurrences of the set of predicates in the unstructured data is determined. A number of occurrences of the expressed relationship in the unstructured data is determined. Using the number of occurrences of the set of predicates and the number of occurrences of the expressed relationship, a confidence value is computed and assigned to the expressed relationship.

Abstract: Boltzmann machine includes a plurality of circuit units each having an adder that adds weighted input signals and a comparison unit that compares an output signal of the adder with a threshold signal to output a binary output signal; and digital arithmetic units each generating the weighted input signals by weighting the binary output signal of the circuit units with a weight. The comparison unit has a first comparator that compares a thermal noise with a reference voltage to output a binary digital random signal, a DA converter that converts the digital random signal to an analog random signal and varies a magnitude of the analog random signal, and a second comparator that compares the output signal of the adder with the analog random signal to generate the binary output signal with a predetermined probability function.

Abstract: Ising devices interconnected via buses each include: neuron circuits that each update, when a value of an output signal from one of connection destination neuron circuits changes, a value based on an update signal; a memory holding connection destination information wherein items of address information respectively identifying the destination neuron circuits and the ising devices including these circuits and identification information about weight values are associated with each other; a control circuit that outputs, when an output signal of a destination neuron circuit in an ising device other than the own ising device changes, the value of the changed output signal and the update signal based on the destination information; and a router that receives a mode set value from a control device and determines whether to connect at least two neighboring ising devices, or a neighboring ising device and the control circuit, based on the set value.

Abstract: A computer-implemented method is provided for facilitating an interaction between a first and second entity. The method includes instructing, by a cognitive recommendation engine, a communication aspect of the interaction between the first entity and the second entity. The instructing includes obtaining, by the cognitive recommendation engine, interaction preference data of the first entity related to the communication aspect of the interaction, and based on the interaction preference data, determining by the cognitive recommendation engine, an instruction recommendation. The instruction recommendation facilitates, from the first entity's view, the interaction between the first and second entities. Further, the instructing includes providing by the cognitive recommendation engine the instruction recommendation to the second entity for use by the second entity during the interaction with the first entity.

Abstract: A method for improving memory efficiency of production rule systems is described. In one embodiment, the method includes identifying a rule associated with production rule systems, constructing a production rule network based at least in part on the rule, identifying a positional constraint associated with the rule, and implementing an alpha memory gate in the production rule network based at least in part on the positional constraint. In some cases, the alpha memory gate is one of a plurality of nodes of the production rule network.

Abstract: A computer-implemented method of training a neural network comprises: identifying, by one or more processors, a set of parameters for a cost function for the neural network; applying, by the one or more processors, a gradient optimization of the cost function to generate a set of intermediate optimized parameter values for the cost function; initializing, by the one or more processors, a simulated annealing optimization of the cost function using the set of intermediate optimized parameter values for the cost function; applying, by the one or more processors, the simulated annealing optimization of the cost function to generate a set of final optimized parameter values for the cost function; assigning, by the one or more processors, the set of final optimized parameter values as weights for connections of the neural network; and using, by the one or more processors, the neural network to perform operations.

Abstract: Systems, technologies and techniques for generating a customized classification model are disclosed. The system and technologies, such as THOMSON REUTERS SELF-SERVICE CLASSIFICATION™, employ part machine learning and part an user interactive approach to generate a customized classification model. The system combines a novel approach for text classification using a smaller initial set of data to initiate training, with a unique workflow and user interaction for customization.

Abstract: The APPARATUSES, METHODS AND SYSTEMS FOR A DIGITAL CONVERSATION MANAGEMENT PLATFORM (“DCM-Platform”) transforms digital dialogue from consumers, client demands and, Internet search inputs via DCM-Platform components into tradable digital assets, and client needs based artificial intelligence campaign plan outputs. In one implementation, The DCM-Platform may capture and examine conversations between individuals and artificial intelligence conversation agents. These agents may be viewed as assets. One can measure the value and performance of these agents by assessing their performance and ability to generate revenue from prolonging conversations and/or ability to effect sales through conversations with individuals.

Abstract: The APPARATUSES, METHODS AND SYSTEMS FOR A DIGITAL CONVERSATION MANAGEMENT PLATFORM (“DCM-Platform”) transforms digital dialogue from consumers, client demands and, Internet search inputs via DCM-Platform components into tradable digital assets, and client needs based artificial intelligence campaign plan outputs. In one implementation, The DCM-Platform may capture and examine conversations between individuals and artificial intelligence conversation agents. These agents may be viewed as assets. One can measure the value and performance of these agents by assessing their performance and ability to generate revenue from prolonging conversations and/or ability to effect sales through conversations with individuals.

Abstract: Systems and methods are described for time-shifting interactions by sharing an artificial intelligence personality (AIP). An AIP is an understanding construct that may control a variety of communication experiences to support a sense of ongoing social connectedness. An AIP may be instantiated within one or more HIEs that interact with humans in a human, cartoon or pet-like manner. HIEs may include robots, robotic pets, toys, simple-to-use devices, and graphical user interfaces. The AIP may be periodically updated based on human interactions sensed by the HIEs as well as knowledge of historical and ongoing events. The systems may provide users with intuitive machine companions that exhibit an expert knowledge base and a familiar, cumulative personality. HIEs may continue to operate without interruption in the presence of telecommunications delays or interruptions, and/or the absence of one or more human participants; allowing participants to “time-shift” their sense of connectedness.

Abstract: Data is classified using corrected semi-supervised data. Cluster centers are defined for unclassified observations. A class is determined for each cluster. A distance value is computed between a classified observation and each cluster center. When the class of the classified observation is not the class determined for the cluster center having a minimum distance, a first distance value is selected as the minimum distance, a second distance value is selected as the distance value computed to the cluster center having the class of the classified observation, a ratio value is computed between the second distance value and the first distance value, and the class of the classified observation is changed to the class determined for the cluster center having the minimum distance value when the computed ratio value satisfies a label correction threshold. A classification matrix is defined using corrected observations to determine the class for the unclassified observations.

Abstract: A neuron circuit can switch between two functions: as an input neuron circuit, and as a hidden neuron circuit. An error circuit can switch between two functions: as a hidden error circuit, and as an output neuron circuit. A switching circuit is configured to be capable of changing the connections between the neuron circuit, a synapse circuit, and the error circuit. The synapse circuit includes an analog memory that stores data that corresponds to the connection strength between the input neuron circuit and the hidden neuron circuit or between the hidden neuron circuit and the output neuron circuit, a writing circuit that changes the data in the analog memory, and a weighting circuit that weights an input signal in reaction to the data of the analog memory and outputs the weighted output signal. The analog memory includes a transistor comprising an oxide semiconductor with extremely low off-state current.

Abstract: Systems and methods are described for time-sharing interactions using a shared artificial intelligence personality (AIP) incorporated within multiple human interaction entities (HIEs). An AIP is an understanding construct that may control a variety of communication experiences to support a sense of ongoing social connectedness. An AIP may be instantiated within two or more HIEs that interact with humans in a human, cartoon or pet-like manner. HIEs may include robots, robotic pets, toys, simple-to-use devices, and graphical user interfaces. The AIP may be periodically updated based on human interactions sensed by the HIEs as well as knowledge of historical and ongoing events. The systems may provide two or more users with intuitive machine companions that exhibit an expert knowledge base and a familiar, cumulative personality.