Abstract: A system and method is disclosed for knowledge engineering using a computerized graphical editor to manage and create knowledge-based systems containing a navigable graph of modal pages with conditional content and user interface knowledge. The invention enables the entire knowledge engineering workflow to be performed within a non-technical graphical environment and without requiring a computer programming or mathematical background. Further, the presentation of knowledge as modal pages allows for simple ontological discovery and end-user player operation. Once editing is complete, the method allows for the set of pages, variables, and settings of which the knowledge-based system is composed to be exported into an independently executable knowledge-based system player containing an embedded inference engine.

Abstract: A plurality of synapse determination circuits are provided on a one-to-one basis for a plurality of gate electrodes of a multi-input gate electrode in a neuron element. With respect to first image regions where “1” is repeatedly inputted in correspondence with group information, the synapse determination circuits corresponding to the first image regions are excitatory synapses. With respect to second image regions where “0” is repeatedly inputted in correspondence with the group information, the synapse determination circuits corresponding to the second image regions are inhibitory synapses.

Abstract: A system and method for processing information in unstructured or structured form, comprising a computer running in a distributed network with one or more data agents. Associations of natural language artifacts may be learned from natural language artifacts in unstructured data sources, and semantic and syntactic relationships may be learned in structured data sources, using grouping based on a criteria of shared features that are dynamically determined without the use of a priori classifications, by employing conditional probability constraints.

Abstract: A method for distributing sets of regular expressions to a fixed number of state machine engines includes combining, with a processing device, a plurality of regular expressions into a single compound regular expression, creating a single nondeterministic finite automaton (NFA) including a plurality of NFA states based on the compound regular expression, performing an interference analysis for each pair of NFA states to identify all pairs of NFA states that would potentially interfere in an equivalent deterministic finite automaton (DFA), creating an interference graph representing the regular expressions associated with potentially interfering NFA states based on the results of the interference analysis, and performing a graph coloring algorithm on the interference graph to assign a different color to each represented regular expression in the graph.

Abstract: A machine learning apparatus includes a state observing unit for observing a state variable comprised of at least one of an actual dimension value, a resistance actual value, etc., and at least one of a dimension command value, a resistance command value, etc., and an execution time command value for a program, and a learning unit for performing a learning operation by linking at least one of an actual dimension value, a resistance actual value, etc., to at least one of a dimension command value, a resistance command value, etc., observed by the state observing unit, and an execution time command value for the program.

Abstract: A control device in a vehicle includes a unit for calculating, during operation of the vehicle, on the basis of at least one input variable ascertained during operation, at least one output variable for a control system of functions of the vehicle. The control device performs the calculation of the output variables using a Bayesian regression of training values ascertained, before operation, for the output variable and the input variable.

Abstract: A machine learning apparatus includes a state observing unit and a learning unit. The state observing unit observes a state variable comprised of at least one of an adhesion state, a dielectric strength voltage, an electric heating time temperature, and an actual electric heating time value of a coil electrically heated by a coil electric heating unit, and at least one of an electric heating time command value, a voltage, and a current in the coil electric heating unit. The learning unit performs a learning operation by linking at least one of an adhesion state, a dielectric strength voltage, an electric heating time temperature, and an actual electric heating time value of the coil observed by the state observing unit to at least one of the electric heating time command value, the voltage, and the current, which are observed by the state observing unit.

Abstract: The technology disclosed relates to manipulating a virtual object. In particular, it relates to detecting a hand in a three-dimensional (3D) sensory space and generating a predictive model of the hand, and using the predictive model to track motion of the hand. The predictive model includes positions of calculation points of fingers, thumb and palm of the hand. The technology disclosed relates to dynamically selecting at least one manipulation point proximate to a virtual object based on the motion tracked by the predictive model and positions of one or more of the calculation points, and manipulating the virtual object by interaction between at least some of the calculation points of the predictive model and the dynamically selected manipulation point.

Abstract: A method and associated systems automatically convert source code of a legacy computer program into a target application by generating an intermediary, context-sensitive, business-process model that represents logic of the legacy system. A set of business rules are inferred from the source code. Each rule is modified by a first generation of additional conditions inferred from context of the source code and of the rule. The rule continues to be further refined by successive generations of context-dependent conditions, identifying each generation of conditions from the context of the preceding generation. This procedure repeats until no more levels of context can be identified. The rule is then imported into the business-process model as a logical data structure. Logic represented by the resulting model is used to generate source code of the target application.

Abstract: A non-deterministic finite state machine module for use in a regular expression matching system. The system includes a computational unit implementing a non-deterministic finite state machine representing a regular expression, wherein the computational unit is configured to: receive an input data stream, wherein an occurrence of the regular expression is determined, and an activation signal; process the input data stream with respect to the non-deterministic finite state machine depending on the activation signal; and provide at least one branch data output for initializing an additional non-deterministic finite state machine module if the processing of an element of the input data stream according to the non-deterministic finite state machine results in a branching of a processing thread.

Abstract: Embodiments of the invention provide a method comprising maintaining a library of one or more compositional prototypes. Each compositional prototype is associated with a neurosynaptic program. The method further comprises searching the library based on one or more search parameters. At least one compositional prototype satisfying the search parameters is selected. A neurosynaptic network is generated or extended by applying one or more rules associated with the selected compositional prototypes.

Abstract: A machine learning apparatus that learns a condition associated with a gain of a magnetic flux controller and a time constant of a magnetic flux estimator in a motor control apparatus includes: a state observation unit that observes a state variable defined by at least one of data relating to an acceleration of a motor, data relating to a jerk of the motor, and data relating to an acceleration time of the motor; and a learning unit that learns the condition associated with the gain of the magnetic flux controller and the time constant of the magnetic flux estimator in accordance with a training data set defined by the state variable.

Abstract: A control method of the present disclosure is a control method of an information processing apparatus which includes a storage unit that stores predetermined resource information related to a resource required for a living activity of a user, the method including: an acquiring step of acquiring device information; an estimating step of estimating a living activity of the user; a competition extracting step of extracting a living activity group including living activities for which the resources are likely to compete among the living activities, based on the resource information; a resource judging step of judging whether a required resource has been secured for each of the living activities, based on the device information and the resource information; and an inhibition extracting step of extracting, as an inhibited living activity, a living activity for which it has been judged that the required resource has not been secured.

Abstract: Production equipment according to an embodiment of the present invention includes a machine learning system and an assembly and test unit. The assembly and test unit chooses components from component groups having different characteristic values, assembles the chosen components into a product, and tests the assembled product. The machine learning system includes a state observer for observing a test result of the product and the inventory amounts of the components grouped based on the characteristic values of the components; a reward calculator for calculating a reward based on the test result and the inventory amounts; an artificial intelligence for determining an action value based on an observation result by the state observer and the reward calculated by the reward calculator; and a decision maker for choosing components to be used in the next product assembly from the component groups based on a determination result by the artificial intelligence.

Abstract: A method and apparatus for providing personalized configuration of physical supports for the human body, comprising accepting input including an individual's demographic information, neurological attributes, physical history, operational environment, and outcome or use objectives, processing user input employing an artificial intelligence engine, and then returning guidance and/or control parameters directed to seating adjustment and positioning, including incline angles for wheelchair tilt and recline.

Abstract: Specification covers new algorithms, methods, and systems for artificial intelligence, soft computing, and deep learning/recognition, e.g., image recognition (e.g., for action, gesture, emotion, expression, biometrics, fingerprint, facial, OCR (text), background, relationship, position, pattern, and object), Big Data analytics, machine learning, training schemes, crowd-sourcing (experts), feature space, clustering, classification, SVM, similarity measures, modified Boltzmann Machines, optimization, search engine, ranking, question-answering system, soft (fuzzy or unsharp) boundaries/impreciseness/ambiguities/fuzziness in language, Natural Language Processing (NLP), Computing-with-Words (CWW), parsing, machine translation, sound and speech recognition, video search and analysis (e.g.

Abstract: Certain relationships representing material insights are identified from among a set of discovered relationships. Cognitive discovery of relationships in a knowledge base, or corpus, are ranked according to one or more metrics indicative of material insights, including recentness and degree of alignment.

Abstract: Methods, systems and apparatus that provide for perceptual, cognitive, and motor behaviors in an integrated system implemented using neural architectures. Components of the system communicate using artificial neurons that implement neural networks. The connections between these networks form representations—referred to as semantic pointers—which model the various firing patterns of biological neural network connections. Semantic pointers can be thought of as elements of a neural vector space, and can implement a form of abstraction level filtering or compression, in which high-dimensional structures can be abstracted one or more times thereby reducing the number of dimensions needed to represent a particular structure.

Abstract: The disclosed embodiments include a method, apparatus, and computer program product for generating facies definition. One embodiment is a computer-implemented method that includes the steps of receiving well logging data indicative of one or more properties of geologic formations penetrated by one or more wellbores, wherein no assumptions are being introduced to the well logging data; determining a type well; developing an appropriate scaling of the well logging data based on the type well; creating a training set by drawing samples from the well logging data at random depths; modifying the training set to remove interfering data; performing an unsupervised classification procedure on the training set to group samples in the training set; comparing a suite of values of the well logging data in the groups to classify lithofacies of the type well; develop classification functions; and classifying unknown wells using the classification functions to generate the facies definition.

Abstract: A first representation of a feature processing recipe is received at a machine learning service. The recipe includes a section in which groups of variables on which common transformations are to be applied are defined, and a section in which a set of transformation operations are specified. The first representation of the recipe is validated based at least in part on a library of function definitions supported by the service, and an executable version of the recipe is generated. In response to a determination that the recipe is to be executed on a particular data set, a set of provider network resources is used to implement a transformation operation indicated in the recipe.