Abstract: An information processing apparatus discloses herein includes an acquiring unit and an estimating unit. The acquiring unit acquires a plurality of pieces of identity information including a keyword based on an operation performed by a user. The estimating unit estimates an age group of the user based on the plurality of pieces of identity information acquired by the acquiring unit.

Abstract: A database query comprising predicates may be received. Each predicate may operate on database columns. The database query may be determined to comprise strict operators. An upper bound neural network may be defined for calculating an adjacent upper bound and a lower bound neural network may be defined for calculating an adjacent lower bound. The upper bound neural network and the lower bound neural network may be trained using a selected value from data of a database table associated with the database query to be executed through the upper bound neural network and the lower bound neural network. The upper bound neural network and the lower bound neural network may be adjusted by passing in an expected value using an error found in expressions. The adjacent lower bound and the adjacent upper bound may be calculated in response to completion of initial training for the database columns.

Abstract: Each processor of the SIMD array performs the computations for a respective neuron of a neural network. As part of this computation, each processor of the SIMD array multiplies an input to a weight and accumulates the result for its assigned neuron each (MAC) instruction cycle. A table in a first memory is used to store which input is fed to each processor of the SIMD array. A crossbar is used to route a specific input to each processor each MAC cycle. A second memory is used to provide the appropriate weight to each processor that corresponds the input being routed to that processor.

Abstract: Some embodiments provide a set of processing units and a set of machine-readable media. The set of machine-readable media stores sets of instructions for applying a network of computation nodes to an input received by the device. The network of computation nodes includes multiple layers of nodes. The set of machine-readable media stores a set of machine-trained weight parameters for configuring the network to perform a specific function. Each layer of nodes has an associated value, and each of the weight parameters is associated with a computation node. Each weight parameter is zero, the associated value for the layer of the computation node with which the weight parameter is associated, or the negative of the associated value for the layer of the computation node with which the weight parameter is associated. Each weight value is stored using two bits or less of data.

Abstract: Embodiments generate digital plans for agricultural fields. In an embodiment, a model receives digital inputs including stress risk data, product maturity data, field location data, planting date data, and/or harvest date data. The model mathematically correlates sets of digital inputs with threshold data associated with the stress risk data. The model is used to generate stress risk prediction data for a set of product maturity and field location combinations. In a digital plan, product maturity data or planting date data or harvest date data or field location data can be adjusted based on the stress risk prediction data. A digital plan can be transmitted to a field manager computing device. An agricultural apparatus can be moved in response to a digital plan.

Abstract: The disclosed technology enables, among other things, the identification of persons and the characterization of mental perceptions (e.g., pain, fatigue, mood) and/or intent (e.g., to perform an action) for medical, safety, home care, and other purposes. Of significance are applications that require long-term patient monitoring, such as tracking disease progression (e.g., multiple sclerosis), or monitoring treatment or rehabilitation efficacy. Therefore, longitudinal data must be acquired over time for the person's identity and other characteristics (e.g., pain level, usage of a cane). However, conventional methods of person identification (e.g., photography) acquire unnecessary personal information, resulting in privacy concerns. The disclosed technology allows measurements to be performed while protecting privacy and functions with partial or incomplete measurements, making it robust to real-world (noisy, uncontrolled) settings, such as in a person's home (whether living alone or with others).

Abstract: An online system actively and randomly selects content items to be labeled for training a classifier. An online system receives content items from client devices of users and selects sets of the content items to be labeled by human labelers. The randomly selected content items are selected at random from the received content items, and the actively selected content items are selected based on the classifier's confidence in accurately predicting the classification of the content items. The online system may use a histogram of content items to actively select content items. The online system assigns the content items to bins of the histogram based on priority scores and selects content items with priority scores of the highest percentile. The online system provides the selected content items to human labelers for labeling. The labeled content items are then used for training the classifier.

Abstract: Live-action event data is received during a live-action event from an event reporting computing system via a computer network interface. The live-action event data is provided to a machine-learning prediction machine previously trained with previously-completed event data to output a prediction for an upcoming aspect of the live-action event. The prediction is sent to a client computing system via the computer network interface prior to commencement of the upcoming aspect to enhance a live-action event experience provided by the client computing system.

Abstract: A predictor management system includes a storage unit 81 and update history management means 82. The storage unit 81 stores, in association with each of a plurality of prediction targets, an update history of a predictor corresponding to the prediction target. The update history management means 82 stores, in response to updating of a predictor, a prediction target of the predictor and an update time of the predictor in the storage unit 81 in association with each other.

Abstract: Embodiments of the present invention disclose a method, computer program product, and system for mitigating machine learning performance digression due to insufficient test data availability. A set of data is received, wherein the received set of data is parsed into a set of training data and a set of test data. A trained model is generated and the trained model is applying to the set of test data. A first set of performance values of the tested trained model are recorded and, if above a threshold, associated with a performance baseline value. A set of modified test data is generated and the trained model is applied to the set of modified test data. A second set of performance values are recorded and a performance difference value is calculated based on the performance baseline value and second set of recorded performance values. A table of results is generated, for display.

Abstract: A vehicle accident detection method and system is provided. The method includes receiving location coordinates associated with a location of an occurring vehicular accident. Data associated with possible causes of the vehicular accident is received from sensors. Traffic related rules associated with a geographical location are retrieved and analyzed with respect to the data. Parameters associated with at least one vehicle involved in the vehicular accident and a possible cause are determined via execution of programming logic and transmitted to additional systems. The possible cause for the vehicular accident is determined from all possible causes based on matching current and historical accident circumstances. Additionally, weighting factors may be available and adjusted over time for accurate accident detection. A possible cause comprising a greatest weighting factor may be used to identify a most likely cause.

Abstract: A computing method receives a labeled sample from an annotator. The method may determine a plurality of reference model risk scores for the first labeled sample, where each reference model risk score corresponds to an amount of risk associated with adding the first labeled sample to a respective reference model of a plurality of reference models. The method may determine an overall risk score for the first labeled sample based on the plurality of reference model risk scores. The method may further determine a probe for confirmation of the first labeled sample and a trust score for the annotator by sending the probe to one or more annotators. In response to determining a trust score for the annotator the method may add the labeled sample to a ground truth or reject the labeled sample.

Abstract: Digital object library management systems and methods for machine learning applications are taught herein.

Abstract: Systems and methods for using a mathematical model based on historical natural language inputs to automatically complete form fields are disclosed. An incident report may be defined with a set of required parameter fields such as category, priority, assignment, and classification. Incident report submission forms may also have other free text input fields providing information about a problem in the natural vocabulary of the person reporting the problem. Automatic completion of these so-called parameter fields may be based on analysis of the natural language inputs and use of machine learning techniques to determine appropriate values for the parameter fields. The machine learning techniques may include parsing the natural language input to determine a mathematical representation and application of the mathematical representation to “match” historically similar input. Once matched the parameter values from the historically similar input may be used instead of generic default values.

Abstract: A process of using a logical entanglement device such as a non-volatile logic gate as a failsafe to constrain the behavior of an autonomous machine controlled by an artificial intelligence (AI). Such a device may be employed to extend an AI self-boundary to include other objects or entities such as humans. This logical entanglement device may act much like a mirror neuron and cause the AI to respond to human nonfunctionality or suffering as if it were its own, causing the AI's behavior to reliably mimic empathy and compassion when interacting with humans and limiting the possibility of the AI devaluing the functionality and well-being of humans.

Abstract: The present invention provides a method of providing information necessary for diagnosing pancreatic cancer using an artificial intelligence-based Bayesian network, comprising: generating a statistical report by learning medical information of a pancreatic cancer patient; constructing a conditional probability table using statistics for each symptom of an actual pancreatic cancer patient; constructing a Bayesian network using the conditional probability table constructed using the statistics for each symptom; applying a Bayesian conditional probability to the Bayesian network; and deriving a probability of getting pancreatic cancer when there is a specific symptom from the pancreatic cancer patient, wherein medical information on pancreatic cancer patients may be statistical data obtained through artificial intelligence or machine learning.

Abstract: A method of augmenting a semantic query of multiple external data sources including receiving a request to search a data store for fields-of-interest designated by a user, applying path-finding technique(s) to identify connections between the fields-of-interest in a semantic model, generating a query based on the connections, intercepting the executing query to determine if data for the fields-of-interest are contained in an external data store, if so identifying an external data service to retrieve external data, executing a semantic query on a triple store, fusing results from the semantic query with the retrieved external data, and providing the fused results to the user computing device. A system and a non-transitory computer readable medium are also disclosed.

Abstract: In a computer-implemented method, an artificial neural network is trained to identify conversation segments, and/or segment portions, within electronic communication documents (e.g., emails). An input layer of the neural network includes input parameters corresponding to different characteristics of text-based content. A first electronic communication document is received, and its text-based content is processed using the trained neural network to generate one or more position indicators for the document. The position indicators include one or more segment indicators denoting positions of one or more conversation segments within the document, and/or one or more segment portion indicators denoting positions of one or more portions of one or more conversation segments within the document. An ordered relationship between the first electronic communication document and one or more other electronic communication documents is determined using the position indicators.

Abstract: Systems and methods improve the performance of a network that has converged such that the gradient of the network and all the partial derivatives are zero (or close to zero) by splitting the training data such that, on each subset of the split training data, some nodes or arcs (i.e., connections between a node and previous or subsequent layers of the network) have individual partial derivative values that are different from zero on the split subsets of the data, although their partial derivatives averaged over the whole set of training data is close to zero. The present system and method can create a new network by splitting the candidate nodes or arcs that diverge from zero and then trains the resulting network with each selected node trained on the corresponding cluster of the data. Because the direction of the gradient is different for each of the nodes or arcs that are split, the nodes and their arcs in the new network will train to be different. Therefore, the new network is not at a stationary point.

Abstract: A method and system for performing real time searches of large alphanumeric data sets including the following steps, combining a cognitive neuromorphic architecture with a neuron based encoding binary filter, wherein building the filter includes encoding input data as a concatenated binary representation, wherein the data becomes a binary value, connecting an axon to a neuron to create a synapse; wherein each binary value includes multiple axons and neurons, determining a weight to each synapse, applying the synaptic weight to the input data to determine an integrated value and determining if the integrated value is greater than or equal to a threshold value.