Abstract: Systems, methods, and computer-readable medium are provided for healthcare analysis. Data corresponding to a plurality of patients is received. The data is parsed to generate normalized data for a plurality of variables, with normalized data generated for more than one variable for each patient. A causal relationship network model is generated relating the plurality of variables based on the generated normalized data using a Bayesian network algorithm. The causal relationship network model includes variables related to a plurality of medical conditions or medical drugs. In another aspect, a selection of a medical condition or drug is received. A sub-network is determined from a causal relationship network model. The sub-network includes one or more variables associated with the selected medical condition or drug. One or more predictors for the selected medical condition or drug are identified.

Abstract: A method, system and computer readable storage medium for cognitive information processing. The cognitive information processing includes receiving data from a plurality of data sources; processing the data from the plurality of data sources to provide cognitively processed insights via a cognitive inference and learning system, the cognitive inference and learning system comprising a cognitive platform, the cognitive platform comprising a development environment, the development environment being implemented to a create custom extension to the cognitive inference and learning system, the custom extension being created via a cognitive design user interface; performing a learning operation to iteratively improve the cognitively processed insights over time; and, providing the cognitively processed insights to a destination, the destination comprising a cognitive application, the cognitive application enabling a user to interact with the cognitive insights.

Abstract: A non-transitory computer readable medium includes computer executable instructions that, when executed, cause at least one processor to train a model to perform at least one of a prediction operation, a diagnostic operation, or a classification operation based on a training dataset, deploy the model in a production computer system to perform the at least one operation on field data, monitor signal data associated with the model, the signal data including specific or derived signal data representing characteristics of an ecosystem in which the model is deployed and new observations in incoming field data, monitor accuracy of the model by applying a statistical tool to a plurality of data points of the signal data, apply a secondary machine learning predictive engine to the plurality of data points of the signal data to predict future data points of the signal data, determine whether the signal data represents an unstable process by identifying future outlier data points from among the plurality of future data

Abstract: An apparatus comprises at least one processing device that is configured to implement a data collection agent and obtain a telemetry data collection specification. The at least one processing device is configured to train the data collection agent based at least in part on the telemetry data collection specification and to obtain an issue description corresponding to a technical support issue associated with a source device of an information processing system. The at least one processing device is configured to generate a telemetry data collection query based at least in part on the obtained issue description using the trained data collection agent and to submit the query to the source device. The at least one processing device is configured to obtain from the source device, telemetry data generated based at least in part on the query and to present the telemetry data to a user via a graphical user interface.

Abstract: The present disclosure provides a method comprising: receiving an input of a geolocation for detection of one or more target objects within an area; retrieving data related to the one or more target objects from a historical detection module, the historical detection module having performed the following: analysis of a plurality of content streams for a plurality of target objects, detection of the plurality of target objects within one or more frames of the plurality of content streams within the predetermined area, and storage of data related to the detected plurality of target objects, aggregating the retrieved data related to the one or more target objects with the following: weather information of the predetermined area, and locational orientation of the user; and predicting, based on the aggregated data, one or more predictions of the geolocation and timeframe for detection of the one or more target objects within the predetermined area.

Abstract: A system for time series analysis using attention models is disclosed. The system may capture dependencies across different variables through input embedding and may map the order of a sample appearance to a randomized lookup table via positional encoding. The system may capture capturing dependencies within a single sequence through a self-attention mechanism and determine a range of dependency to consider for each position being analyzed. The system may obtain an attention weighting to other positions in the sequence through computation of an inner product and utilize the attention weighting to acquire a vector representation for a position and mask the sequence to enable causality. The system may employ a dense interpolation technique for encoding partial temporal ordering to obtain a single vector representation and a linear layer to obtain logits from the single vector representation. The system may use a type dependent final prediction layer.

Abstract: System and methods for automatically providing action recommendations are described. A method may include collecting a set of telemetry data from a client application. The set of telemetry data contains a plurality of pages generated based on a plurality of user actions performed on the client application. The method may include generating a plurality of prior probabilities corresponding to the plurality of pages and the plurality of user actions. In response to the client application displaying a first page, the method may generating a plurality of posterior probabilities for a subset of user actions that can be invoked in the client application from the first page, and selecting a plurality of recommended actions from the subset of user actions for having the highest corresponding posterior probabilities among the plurality of posterior probabilities.

Abstract: Systems, methods, and devices for pruning a convolutional neural network (CNN). A subset of layers of the CNN is chosen, and for each layer of the subset of layers, how salient each filter in the layer is to an output of the CNN is determined, a subset of the filters in the layer is determined based on the salience of each filter in the layer, and the subset of filters in the layer is pruned. In some implementations, the layers of the subset of layers of the CNN are non-contiguous. In some implementations, the subset of layers includes odd numbered layers of the CNN and excludes even numbered layers of the CNN. In some implementations, the subset of layers includes even numbered layers of the CNN and excludes odd numbered layers of the CNN.

Abstract: Computing resources are optimally allocated for a multipath neural network using a multipath neural network analyzer that includes an interface and a processing device. The interface receives a multipath neural network that includes two or more paths. A first path includes one or more layers. A first layer of the first path corresponds to a first kernel that runs on a compute unit that includes two or more cores. The processing device allocates to the first kernel a minimum number of cores of the compute unit and a maximum number of cores of the compute unit. The minimum number of cores of the compute unit is allocated based on the first kernel being run concurrently with at least one other kernel on the compute unit and the maximum number of cores of the compute unit is allocated based on the first kernel being run alone on the compute unit.

Abstract: A cognitive information processing system environment which includes a plurality of data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; and, a destination, the destination receiving the cognitively processed insights.

Abstract: A cognitive information processing system environment which includes a plurality of data sources; a cognitive inference and learning system coupled to receive a data from the plurality of data sources, the cognitive inference and learning system processing the data from the plurality of data sources to provide cognitively processed insights, the cognitive inference and learning system further comprising performing a learning operation to iteratively improve the cognitively processed insights over time; and, a destination, the destination receiving the cognitively processed insights.

Abstract: A non-transitory computer-readable recording medium stores therein a learning program for causing a computer to execute a process comprising: referring to, at time of learning a computation model that is a target of deep learning and has a plurality of nodes, a storage unit in which route information that indicates a calculation route followed by a tensor in each stage of learning prior to the time of learning, and statistical information regarding a position of a decimal point used in the calculation route are associated with each other; acquiring, when executing each piece of calculation processing set in each of the plurality of nodes at the time of learning, the statistical information corresponding to the route information that reaches each of the plurality of nodes; and executing the each piece of calculation processing using the position of the decimal point specified by the acquired statistical information.

Abstract: A computing device includes at least one processor and at least one module, operable by the at least one processor to receive an communication, determine, based at least in part on the communication, one or more candidate responses to the communication, and receive an indication of user input that selects a candidate response from the one or more candidate responses. The at least one module may be further operable by the at least one processor, responsive to receiving the indication of user input that selects the candidate response, to send the candidate response, determine, based at least in part on at least one of the candidate response and the communication, an operation that is contextually related to the candidate response, and execute the operation.

Abstract: Hardware and methods for neural network processing are provided. A method in a hardware node including a pipeline having a matrix vector unit (MVU), a first multifunction unit connected to receive an input from the matrix vector unit, a second multifunction unit connected to receive an output from the first multifunction unit, and a third multifunction unit connected to receive an output from the second multifunction unit is provided. The method includes performing using the MVU a first type of instruction that can only be performed by the MVU to generate a first result. The method further includes performing a second type of instruction that can only be performed by one of the multifunction units and generating a second result and without storing the any of the two results in a global register, passing the second result to the second multifunction and the third multifunction unit.

Abstract: Systems and methods may reduce bias in an artificial intelligence model. The system may receive word embedding model generated based on a corpus of words. The system may determine a bias definition vector in an embedding space of the word embedding model. The system may receive bias classification criteria. The bias classification criteria may include logic to group word vectors in the word embedding model based on a distance measurement from the bias definition vector. The system may identify, in the word embedding model, a first group of vectors and a second group of vectors based on the bias classification criteria and the bias definition vector. The system may generate a debiased artificial intelligence model. The debiased artificial intelligence model may include associations between words and metrics. The system may weight the metrics for the words associated with the first and second group of vectors with a non-zero penalization factor.

Abstract: The field of the disclosure relates generally to a method and system for analyzing behavior of a computer infrastructure and the displaying the behavior of the computer infrastructure in a graphical manner. The system comprises an analytical engine connected to agents running on devices in the computer infrastructure and analyzing continuous data and asynchronous data.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining neural network architectures. One of the methods includes generating, using a controller neural network having controller parameters and in accordance with current values of the controller parameters, a batch of output sequences. The method includes, for each output sequence in the batch: generating an instance of a child convolutional neural network (CNN) that includes multiple instances of a first convolutional cell having an architecture defined by the output sequence; training the instance of the child CNN to perform an image processing task; and evaluating a performance of the trained instance of the child CNN on the task to determine a performance metric for the trained instance of the child CNN; and using the performance metrics for the trained instances of the child CNN to adjust current values of the controller parameters of the controller neural network.

Abstract: In one aspect, the invention comprises a system and method for control of a transaction state system utilizing a distributed ledger. First, the system and method includes an application plane layer adapted to receive instructions regarding operation of the transaction state system. Preferably, the application plane layer is coupled to the application plane layer interface. Second, a control plane layer is provided, the control plane layer including an adaptive control unit, such as a cognitive computing unit, artificial intelligence unit or machine-learning unit. Third, a data plane layer includes an input interface to receive data input from one or more data sources and to provide output coupled to a decentralized distributed ledger, the data plane layer is coupled to the control plane layer. Optionally, the system and method serve to implement a smart contract on a decentralized distributed ledger.

Abstract: Briefly, embodiments of methods and/or systems of generating preference indices for contiguous portions of digital images are disclosed. For one embodiment, as an example, parameters of a neural network may be developed to generate object labels for digital images. The developed parameters may be transferred to a neural network utilized to generate signal sample value levels corresponding to preference indices for contiguous portions of digital images.

Abstract: As described, an artificial intelligence (AI) design application exposes various tools to a user for generating, analyzing, evaluating, and describing neural networks. The AI design application includes a network generator that generates and/or updates program code that defines a neural network based on user interactions with a graphical depiction of the network architecture. The AI design application also includes a network analyzer that analyzes the behavior of the neural network at the layer level, neuron level, and weight level in response to test inputs. The AI design application further includes a network evaluator that performs a comprehensive evaluation of the neural network across a range of sample of training data. Finally, the AI design application includes a network descriptor that articulates the behavior of the neural network in natural language and constrains that behavior according to a set of rules.