Abstract: The present disclosure relates to a processor for implementing artificial neural networks, for example, convolutional neural networks. The processor includes a memory controller group, an on-chip bus and a processor core, wherein the processor core further includes a register map, an instruction module, a data transferring controller, a data writing scheduling unit, a buffer pool, a data reading scheduling unit and a computation module. The processor of the present disclosure may be used for implementing various neural networks with increased computation efficiency.

Abstract: Techniques are described for applying what-f analytics to simulate performance of computing resources in cloud and other computing environments. In one or more embodiments, a plurality of time-series datasets are received including time-series datasets representing a plurality of demands on a resource and datasets representing performance metrics for a resource. Based on the datasets at least one demand propagation model and at least one resource prediction model are trained. Responsive to receiving an adjustment to a first set of one or more values associated with a first demand: (a) a second adjustment is generated for a second set of one or more values associated with a second demand; and (b) a third adjustment is generated for a third set of one or more values that is associated with the resource performance metric.

Abstract: Methods, systems, and apparatuses for performing target parameter analysis for an assembly line including a plurality of stations. One method includes receiving, with an electronic processor, training data associated with the assembly line. The training data including a plurality of attributes. The method also includes receiving, with the electronic processor, value addition data for each of the plurality of stations. The value addition data for each of the plurality of stations specifying a non-negative value added by each of the plurality of stations. The method also includes learning, with the electronic processor, a decision tree based on the training data and the value addition data. The method also includes performing the target parameter analysis based on the decision tree.

Abstract: The arithmetic processing circuit includes a first layer configured to dispose a learning neural network to compute a coefficient to be set in a recognition neural network, configured to recognize input data by using the coefficient computed on a basis of a recognition result of the recognition neural network with for the input data serving as a reference for computing the coefficient and a recognition result serving as a reference for the input data serving as the reference. The circuit further includes a second layer configured to dispose the recognition neural network to recognize the input data by the coefficient computed by the learning neural network. The circuit still further includes a third layer disposed between the first layer and the second layer, and configured to dispose a memory connected to both of the learning neural network and the recognition neural network.

Abstract: A system for textual analysis of task performances. The system includes a receiving module operating on at least a server configured to receive at least a request for a task performance. The system includes a language processing module operating on the at least a server configured to parse the at least a request for a task performance and retrieve at least a task performance datum, categorize the at least a request for a task performance to at least a task performance list, and assign the at least a request for a task performance to a task performance owner. The system includes a task generator module configured to generate at least a task performance data element containing a task performance list label and a priority label.

Abstract: Techniques for identifying data for use in a healthcare application are described. In an example, a server can receive, via a graphical user interface (GUI), first instructions defining rule(s) associated with an outcome that is relevant to a healthcare application and second instructions defining a schema for translating the rule(s) into logical expression(s). The server can access a plurality of data entries and can apply the logical expression(s) to the plurality of data entries to identify a first data entry that satisfies the rule(s). At a substantially same time, the server can apply a model to the plurality of data entries to identify a second data entry that is predicted to be associated with the outcome. The server can generate an output identifying the first data entry and the second data entry and can utilize the output for the healthcare application.

Abstract: Systems and methods for generating real-time, personalized recommendations are disclosed. In one embodiment, a method operates upon an electronic data collection organized as a network of vertices and edge connections between the vertices. The method provides the recommendations includes iteratively traversing across edges that satisfy search criteria to a new set of vertices and filtering each new set of vertices to satisfy the search criteria. At the conclusion of the traversing and filtering, a final set of vertices represents the recommended entities. In some embodiments, a control vector describes a sequence of relationships between a requester and the items to be recommended. The method can assign scores to candidate recommendations and select the recommendations having the highest scores. Advantageously, the method provides flexibility and rapid execution of recommendation queries without the need to precompute intermediate results.

Abstract: The multiple independent processes run in an AI engine on its cloud-based platform. The multiple independent processes are configured as an independent process wrapped in its own container so that multiple instances of the same processes can run simultaneously to scale to handle one or more users to perform actions. The actions to solve AI problems can include 1) running multiple training sessions on two or more AI models at the same time, 2) creating two or more AI models at the same time, 3) running a training session on one or more AI models while creating one or more AI models at the same time, 4) deploying and using two or more trained AI models to do predictions on data from one or more data sources, 5) etc. A service handles scaling by dynamically calling in additional computing devices to load on and run additional instances of one or more of the independent processes as needed.

Abstract: Technical solutions are described for analyzing a natural language conversation-generating machine. The computer implemented method includes identifying, from a conversation log, a plurality of adjacency pairs. The method further includes determining, from the adjacency pairs, a number of adjacency pairs with outcome success indicators. The method further includes determining, from the adjacency pairs, a number of adjacency pairs with outcome failure indicators. The method further includes computing a mutual understanding score for the machine by computing a ratio of the number of adjacency pairs with outcome success indicators and the number of adjacency pairs with outcome failure indicators.

Abstract: A system includes a knowledge canvassing system executed by a computer, a processor, and a memory coupled to the processor. The memory is encoded with instructions that when executed cause the processor to provide a training system configured to generate benchmark data, each benchmark datum including a set of one or more benchmark input entities and a set of one or more benchmark output entities associated with the one or more benchmark input entities, query the knowledge canvassing system with each set of benchmark input entities, receive, for each set of benchmark input entities queried, an output result from the knowledge canvassing system that includes a set of zero or more knowledge canvassing system output entities, and generate an evaluation score for each set of knowledge canvassing system output entities based on a comparison of the knowledge canvassing system output entities with the set of benchmark output entities.

Abstract: The present disclosure is for systems and methods for connecting offline machine learning training systems with online near-real time machine learning scoring systems. It is not trivial to connect an offline training environment with an online scoring environment. For example, offline training environments are usually static and contain large amounts of historical data that is needed for the initial training of models. Once trained, the model algorithms are then migrated into an online scoring environment for transactional or event based scoring. This migration effectively breaks the connection between the data in the offline environment and the model now running in the online environment. When new or shifting data occurs in the online environment, the static model running in the online environment goes unaltered to the changing inputs. The present disclosure solves the issues that are caused by the break in the offline and online environments.

Abstract: Selecting data from a source text corpus for training a semantic data analysis system includes selecting an item of the text corpus, validating the item, extracting at least one section of the item, determining a length of each of the at least one section of the item, and subdividing each of the sections having a length greater than a predetermined amount into a plurality of fragments that are deemed to be similar. The predetermined amount may be approximately twice a size of a fragment. A fragment may have approximately 100 words or between 40 and 60 words. Fragments from different items may be deemed to be dissimilar. Sections having a length less than the predetermined amount may be ignored. Validating the item may include parsing editorial notes and other accompanying data. The source text corpus may be Wikipedia. The item may be an article.

Abstract: A mood score calculation system includes a processor and a memory. The memory is configured to hold relationship information on a relationship between a mood score of a user and a statistical feature for indicating fluctuation of operational interval time of a user terminal. The processor is configured to acquire an operation log of a first user terminal. The processor is configured to calculate, from the operation log, a value of a statistical feature for indicating fluctuation of operational interval time of the first user terminal. The processor is configured to determine a mood score of a user of the first user terminal based on the value of the statistical feature and the relationship information and output the mood score.

Abstract: With respect to an input data set which contains observation records of a time series, a statistical model which utilizes a likelihood function comprising a latent function is generated. The latent function comprises a combination of a deterministic component and a random process. Parameters of the model are fitted using approximate Bayesian inference, and the model is used to generate probabilistic forecasts corresponding to the input data set.

Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine configured to operate with multiple user interfaces to accommodate different types of users solving different types of problems with AI. The AI engine can include AI-engine modules including an architect module, an instructor module, and a learner module. An assembly code can be generated from a source code written in a pedagogical programming language. The architect module can be configured to propose a neural-network layout from the assembly code; the learner module can be configured to build the AI model from the neural-network layout; and the instructor module can be configured to train the AI model built by the learner module. The multiple user interfaces can include an integrated development environment, a web-browser interface, or a command-line interface configured to enable an author to define a mental model for the AI model to learn.

Abstract: An electronic device includes an address acquisition unit that performs communication with an approaching communication terminal to acquire a unique address of the communication terminal, and a power control unit that shifts its own power state from a standby state where power is supplied only to a minimum function to a quick start state where power is supplied to a function other than a presentation function to a user, when the unique address acquired by the address acquisition unit matches a registered address.

Abstract: Provided herein in some embodiments is an artificial intelligence (“AI”) engine hosted on one or more servers configured to cooperate with one or more databases including one or more AI-engine modules. The one or more AI-engine modules include an architect module configured to propose an AI model from an assembly code. The assembly code can be generated from a source code written in a pedagogical programming language describing a mental model of one or more concept modules to be learned by the AI model and curricula of one or more lessons for training the AI model on the one or more concept modules in one or more training cycles. The AI engine can be configured to instantiate a trained AI model based on the one or more concept modules learned by the AI model in the one or more training cycles.

Abstract: Systems and method for computing yield values through a neural network from a plurality of different data inputs are disclosed. In an embodiment, a server computer system receives a particular dataset relating to one or more agricultural fields wherein the particular data set comprises particular crop identification data, particular environmental data, and particular management practice data. Using a first neural network, the server computer system computes a crop identification effect on crop yield from the particular crop identification data. Using a second neural network, the server computer system computes an environmental effect on crop yield from the particular environmental data. Using a third neural network, the server computer system computes a management practice effect on crop yield from the management practice data.

Abstract: Systems and methods of predicting documentation associated with an encounter between attendees are provided. For instance, attendee data indicative of one or more previous visit notes associated with a first attendee can be obtained. The attendee data can be inputted into a machine-learned note prediction model that includes a neural network. The neural network can generate one or more context vectors descriptive of the attendee data. Data indicative of a predicted visit note can be received as output of the machine-learned note prediction model based at least in part on the context vectors. The predicted visit note can include a set of predicted information expected to be included in a subsequently generated visit note associated with the first attendee.

Abstract: Computer-implemented methods are disclosed for estimating values of hemodynamic forces acting on plaque or lesions. One method includes: receiving one or more patient-specific parameters of at least a portion of a patient's vasculature that is prone to plaque progression, rupture, or erosion; constructing a patient-specific geometric model of at least a portion of a patient's vasculature that is prone to plaque progression, rupture, or erosion, using the received one or more patient-specific parameters; estimating, using one or more processors, the values of hemodynamic forces at one or more points on the patient-specific geometric model, using the patient-specific parameters and geometric model by measuring, deriving, or obtaining one or more of a pressure gradient and a radius gradient; and outputting the estimated values of hemodynamic forces to an electronic storage medium. Systems and computer readable media for executing these methods are also disclosed.