A computer-implemented visual interface for identifying and revealing objects from video-based media provides visual cues to enable users to interact with video-based media. Objects in videos are inferred and identified based upon automatic interpretations of the video and/or audio that is associated with the video. The automatic interpretations may be performed by a computer-implemented neural network. The computer-implemented visual interface is integrated with the video to enable users to interact with the identified objects. User interactions with the visual interface may be through either touch or non-touch means. Information is delivered to users that is based upon the identified objects, including in augmented or virtual reality-based form, responsive to user interactions with the computer-implemented visual interface.

A system for smart pooling includes a computing device configured to obtain a feature datum, identify a predictive prevalence value as a function of the feature datum, wherein identifying the predictive prevalence value further comprises receiving a predictive training set correlating the feature datum with a probabilistic outcome, training a predictive machine-learning model as a function of the predictive training set, and identifying the predictive prevalence value as a function of the trained predictive machine-learning model and the feature datum, and determine an enhanced well count.

Techniques for tuning a machine learning algorithm using automatically determined optimal hyperparameters are described. An exemplary method includes receiving a request to determine a search space for at least one hyperparameter of a machine learning algorithm; determining, according to the request, optimal hyperparameter values from the search space for at least the one hyperparameter of the machine learning algorithm based on an evaluation of hyperparameters from the same machine learning algorithm on different datasets; and tuning the machine learning algorithm using the determined optimal hyperparameter values for the at least one hyperparameter of the machine learning algorithm to generate a machine learning model.

Disclosed is an artificial intelligence or machine learning algorithm that may be applied to a plurality of machine learning devices in a 5G environment connected to perform the Internet of things. A machine learning method by a first learning machine according to one embodiment of the present disclosure may include obtaining input data; determining, from among a plurality of clusters, a cluster to which the input data belongs, by using a first artificial neural network; transmitting a plurality of sample features associated with the determined cluster to a second learning device using a second artificial neural network; receiving a label for the plurality of sample features from the second learning device, in response to the transmission; and associating the received label with the determined cluster.

Embodiments for implementing mixed precision learning for neural networks by a processor. A neural network may be replicated into a plurality of replicated instances and each of the plurality of replicated instances differ in precision used for representing and determining parameters of the neural network. Data instances may be routed to one or more of the plurality of replicated instances for processing according to a data pre-processing operation.

A method and a system for optimizing problem-solving based on probabilistic bit circuits are provided. The method includes: performing a modeling transformation on an objective problem to obtain a corresponding Hamiltonian relationship; obtaining a column Hamiltonian of said probabilistic bit circuit based on said Hamiltonian relationship; and performing parallel annealing iterations on multicolumn Hamiltonian based on row-flipping operations on said probabilistic bit circuits to obtain an updated probabilistic bit configuration, so as to achieve optimization of said problem.

Aspects described herein relate to a centralized computing system that interacts with a plurality of data centers, each having an edge server. Each edge server obtains sensor information from a plurality of sensors and processes the sensor information to detect an imminent shutdown and sends emergency data to a centralized processing entity when detected. In order to make a decision, the edge server processes the sensor data based on dynamic sensor thresholds and dynamic prioritizer data by syncing with the centralized computing system. Because of the short time duration to report emergency data before an imminent complete shutdown, an edge server may utilize a quantum data pipeline and quantum data storage as a key medium for all data transfer in a normal condition and at the time of emergency for internally transporting processed sensor data and providing the emergency data to the centralized processing entity.

A computer-implemented method of improving maintenance of a complex system, the complex system having a plurality of components, the method involving: preparing data across a plurality of data streams from a plurality of data sources; generating a matrix representation of the data; calculating a time proximity of the data; calculating a plurality of corresponding cell values of the matrix representation; matching event information across the plurality of data streams from a plurality of data sources, the plurality of data sources corresponding to the plurality of components, wherein at least one data stream of the plurality of data streams has at least one of low fidelity data and imprecise event generation information; and scoring the imprecise event generation information across the plurality of data streams, thereby providing a score indicating a match quality of the imprecise event generation information.

A method and system for recognizing environmental protection equipment based on a deep hierarchical fuzzy algorithm. The method includes the following steps: (1) acquiring harmonic signal data of the environmental protection equipment by harmonic detectors, and acquiring type information of corresponding environmental protection equipment on site for constructing a training sample database; (2) extracting a feature vector of the data in the training sample database by a local mean decomposition method, and training, by using the training sample database, a deep hierarchical fuzzy system constructed on the basis of a least square method, so as to construct a recognition model; and (3) evaluating the inputted harmonic signal data by using the recognition model to determine whether inspected equipment is the corresponding environmental protection equipment.

Disclosed are an apparatus, a system and a non-transitory computer readable medium that implement processing circuitry that receives non-dialog information from a smart device and determines a data type of data in the received non-dialog information. Based on the determined data type, the processing circuitry transforms the received first data using an input from a machine learning algorithm into transformed data. The transformed data is standardized data that is palatable for machine learning algorithms such as those used implemented as chatbots. The standardized transformed data is useful for training multiple different chatbot systems and enables the typically underutilized non-dialog information to be used to as training input to improve context and conversation flow between a chatbot and a user.