Abstract: Architectures for multicore neuromorphic systems are provided. In various embodiments, a neural network description is read. The neural network description describes a plurality of logical cores. A plurality of precedence relationships are determined among the plurality of logical cores. Based on the plurality of precedence relationships, a schedule is generated that assigns the plurality of logical cores to a plurality of physical cores at a plurality of time slices. Based on the schedule, the plurality of logical cores of the neural network description are executed on the plurality of physical cores.

Abstract: Disclosed herein are system, method, and computer program product embodiments for encoding symbolic data into a subsymbolic format while preserving the semantic arrangement of the symbolic data. In an embodiment, to encode the symbolic data, a subsymbolic encoder system may convert a symbolic graph into a tuple representation having tuple elements corresponding to the nodes of the symbolic graph. The subsymbolic encoder system may retrieve a dictionary identification for each tuple element and calculate a subsymbolic value for each tuple element using an exponential component. The subsymbolic encoder system may standardize the length of the subsymbolic values and/or add a weighted relationship indicator to the subsymbolic values. The subsymbolic encoder system may transmit the subsymbolic values to a subsymbolic intelligence system.

Abstract: Methods, systems, and apparatus for prioritizing communications are described. Metadata that characterizes an electronic communication is obtained and a machine learning algorithm is applied to the metadata to generate a scoring model. A score for the electronic communication is generated based on the scoring model.

Abstract: A method for selecting an action, includes reading, into a memory, a Partially Observed Markov Decision Process (POMDP) model, the POMDP model having top-k action IDs for each belief state, the top-k action IDs maximizing expected long-term cumulative rewards in each time-step, and k being an integer of two or more, in the execution-time process of the POMDP model, detecting a situation where an action identified by the best action ID among the top-k action IDs for a current belief state is unable to be selected due to a constraint, and selecting and executing an action identified by the second best action ID among the top-k action IDs for the current belief state in response to a detection of the situation. The top-k action IDs may be top-k alpha vectors, each of the top-k alpha vectors having an associated action, or identifiers of top-k actions associated with alpha vectors.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using recurrent neural networks. One of the systems includes a main recurrent neural network comprising one or more recurrent neural network layers and a respective hyper recurrent neural network corresponding to each of the one or more recurrent neural network layers, wherein each hyper recurrent neural network is configured to, at each of a plurality of time steps: process the layer input at the time step to the corresponding recurrent neural network layer, the current layer hidden state of the corresponding recurrent neural network layer, and a current hypernetwork hidden state of the hyper recurrent neural network to generate an updated hypernetwork hidden state.

Abstract: A system for generating alerts including processors and storage devices. The instructions configure the one or more processors to perform operations, which include receiving an event from a data stream, extracting keys from the event, associating the event with at least one account based on the extracted keys, identifying a state variable associated with the at least one account, updating the state variable by accumulating the event in the state variable, registering a time stamp for the event in the state variable, and retiring expired events from the state variable. The operations may also include determining whether the state variable is above a threshold level and generating an alert for the account when the state variable is above the threshold level.

Abstract: A user interface may be presented to a creator to facilitate the creation of narrative content. The user interface may be part of a system configured to generate recommendations pertaining to narrative content. The narrative content is meant to be experienced by users, e.g., in a virtual space. Feedback and/or other responses from the creator may be used to train and/or modify the generation of new recommendations. Feedback and/or other responses from the users may be used to train and/or modify the generation of new recommendations.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing operations using data from a data source. In one aspect, a method includes a neural network system including a controller neural network configured to: receive a controller input for a time step and process the controller input and a representation of a system input to generate: an operation score distribution that assigns a respective operation score to an operation and a data score distribution that assigns a respective data score in the data source. The neural network system can also include an operation subsystem configured to: perform operations to generate operation outputs, wherein at least one of the operations is performed on data in the data source, and combine the operation outputs in accordance with the operation score distribution and the data score distribution to generate a time step output for the time step.

Abstract: A system for remote monitoring of a machine is provided. The system includes a data store to store machine data associated with an operation of the machine. The system includes an analyzer comprising a plurality of analytics engines to analyze the machine data. The analyzer selects one or more analytics engines based at least on one of machine data and a type of the machine. The analyzer is further configured to analyze machine data using the selected one or more analytics engines and to determine a plurality of exceptions. The system includes a rules engine to process at least two of the plurality of exceptions and determine a smart exception, wherein the smart exception is a hierarchical combination of the at least two of the plurality of exceptions. The system includes an interface to display a notification to a user in the event of a smart exception.

Abstract: Techniques for generating a personality trait model are described. According to an example, a system is provided that can generate text data and linguistic data, and apply psycholinguistic data to the text data and the linguistic data, resulting in updated text data and updated linguistic data. The system is further operable to combine the updated text data with the updated linguistic data to generate a personality trait model. In various embodiments, the personality trait model can be trained and updated as additional data is received from various inputs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training a machine learning model. In one aspect, a method includes receiving training data for training the machine learning model on a plurality of tasks, where each task includes multiple batches of training data. A task is selected in accordance with a current task selection policy. A batch of training data is selected from the selected task. The machine learning model is trained on the selected batch of training data to determine updated values of the model parameters. A learning progress measure that represents a progress of the training of the machine learning model as a result of training the machine learning model on the selected batch of training data is determined. The current task selection policy is updated using the learning progress measure.

Abstract: A computer implemented method for training a neural network to capture a structural feature specific to a set of chemical compounds is disclosed. In the method, the computer system reads an expression describing a structure of the chemical compound for each chemical compound in the set and enumerates one or more combinations of a position and a type of a structural element appearing in the expression for each chemical compound in the set. The computer system also generates training data based on the one or more enumerated combinations for each chemical compound in the set. The training data includes one or more values with a length, each of which indicates whether or not a corresponding type of the structural element appears at a corresponding position for each combination. Furthermore, the computer system trains the neural network based on the training data for the set of the chemical compounds.

Abstract: A computer-implementable method for managing a cognitive graph comprising: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element of the collection of knowledge elements being persisted in its original form.

Abstract: A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus. The computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data; and, generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph, the generating the cognitive insight using an insight agent to access the collection of knowledge elements.

Abstract: A computer-implementable method for generating an insight comprising: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data; and, generating a cognitive insight based upon the collection of knowledge elements stored within the cognitive graph, the generating the cognitive insight using an insight agent to access the collection of knowledge elements.

Abstract: A system comprising: a processor; a data bus coupled to the processor; and a non-transitory, computer-readable storage medium embodying computer program code, the non-transitory, computer-readable storage medium being coupled to the data bus. The computer program code interacting with a plurality of computer operations and comprising instructions executable by the processor and configured for: receiving data from a plurality of data sources; processing the data from the plurality of data sources, the processing the data from the plurality of data sources identifying a plurality of knowledge elements; and, storing the knowledge elements within the cognitive graph as a collection of knowledge elements, the storing universally representing knowledge obtained from the data, each knowledge element of the collection of knowledge elements being persisted in its original form.

Abstract: A method and associated systems for a self-learning energy switch. The switch creates an array of cognitive models for each candidate energy source. Each array returns a probability that its corresponding source is the most cost-effective and operationally suitable energy supplier at that time. Each model in an array contributes to the array's returned probability as a function of a corresponding class of decision-making factors. The system fine-tunes the models by weighting them as functions of extrinsic evidentiary information that may imply future behavior of the decision-making factors and combines each model's returned probabilities to select an optimal energy source. The system then automatically routes power from the optimal source to a consumer's energy-consuming premises. This self-learning procedure repeats indefinitely, continuously tuning the models in response to identifying additional extrinsic evidence and reasons why the system's previous energy selections were either optimal or non-optimal.

Abstract: Mechanisms are provided to implement a self-training engine of a question and answer system. The self-training engine pairs an unanswered natural language question with portions of an electronic document to generate an unlabeled data set. The self-training engine trains a model based on a labeled data set comprising a finite number of question and answer pair data structures and applies the model to the unlabeled data set to identify an answer from the portions of the electronic document to the unanswered natural language question. The self-training engine modifies the labeled data set to include the identified answer and corresponding unanswered natural language question as an additional question and answer pair data structure. The self-training engine then trains the model based on the modified labeled data set.

Abstract: A system, method and computer program product for disambiguating one or more entity mentions in one or more documents. The method facilitates the simultaneous linking entity mentions in a document based on convolution neural networks and recurrent neural networks that model both the local and global features for entity linking. The framework uses the capacity of convolution neural networks to induce the underlying representations for local contexts and the advantage of recurrent neural networks to adaptively compress variable length sequences of predictions for global constraints. The RNN functions to accumulate information about the previous entity mentions and/or target entities, and provide them as the global constraints for the linking process of a current entity mention.

Abstract: A data classification device includes an estimation unit that estimates, for each of one or more classes provided for learning data pieces in a feature-amount-data space that includes multiple learning data pieces, probability densities of learning data pieces belonging to the class and learning data pieces not belonging to the class around a judgment target data piece in the feature-amount-data space, a calculation unit that calculates, based on the probability densities, an index indicating how much the judgment target data piece is likely to belong to the class, and a judgment unit that judges which class the judgment target data piece belongs to by using the index. Based on distribution of positive data pieces belonging to the class and negative data pieces not belonging to the class around the judgment target data piece, the estimation unit determines a size of a region used for the estimation.