A classification apparatus is configured to perform a classification using a neural network with at least one hidden layer and an output layer, wherein the classification apparatus comprises a coarse training unit configured to train the neural network on a subset of neurons of a last hidden layer and a set of neurons of the output layer; and a fine training unit configured to train the neural network on a set of the last hidden layer and a subset of neurons of the output layer. By executing the classification apparatus, training of a classification model can be improved by reducing the computational burden of the classification, speeding up the training time of a classification model, and speeding up the inference time during application of the classification model.

A method of providing an intelligent response on an electronic device and an apparatus therefor are provided. The method includes receiving a command from a user of the electronic device, analyzing the command, generating, based on the analyzed command, at least one intelligent response corresponding to the command and an actual response corresponding to the command, the at least one intelligent response including at least one of a sound effect associated with the command, a vibration effect associated with the command, or a visual effect associated with the command, and outputting the at least one intelligent response and the actual response by the electronic device.

A system and method of configuring a graphical control structure for controlling a machine learning-based automated dialogue system includes configuring a root dialogue classification node that performs a dialogue intent classification task for utterance data input; configuring a plurality of distinct dialogue state classification nodes that are arranged downstream of the root dialogue classification node; configuring a graphical edge connection between the root dialogue classification node and the plurality of distinct state dialogue classification nodes that graphically connects each of the plurality of distinct state dialogue classification nodes to the root dialogue classification node, wherein (i) the root dialogue classification node, (ii) the plurality of distinct classification nodes, (iii) and the transition edge connections define a graphical dialogue system control structure that governs an active dialogue between a user and the machine learning-based automated dialogue system.

Log data associated with an environment that includes containers is received. An example of such an environment is one managed by Kubernetes. A logical graph is generated using at least a portion of the received log data. The logical graph is used to detect an anomaly. In response to the anomaly being detected, the anomaly is recorded.

In an approach to deriving highly accurate models, one or more computer processors train a set of machine learning models utilizing a training set and a deep learning algorithm; generate one or more feedback data sets for each model in the set of trained models; rank each model in the set of trained models based on the generated feedback data sets; dynamically adjust one or more thresholds, that initiate a retraining or deployment of one or more ranked models, based, at least in part, on one or more production environment requirements; responsive to exceeding one or more adjusted thresholds, automatically deploy one or more ranked models to one or more deployment environments based, at least in part, on the ranking of the one or more trained models; responsive to not exceeding one or more adjusted thresholds, retrain each model in the set of trained models.

Embodiments of the invention are directed to systems, methods, and computer program products for an electronic system for management of image processing model database. The system is configured for versioning machine-learning neural-network based image processing models and identifying and tracking mutations in hyper parameters amongst versions of image processing models. The system is configured to determine that a second image processing model is a version of a first image processing model. The system is further configured to map the mutations in hyper parameters between the first plurality of hyper parameters of the first image processing model and the second plurality of hyper parameters associated with the second image processing model.

Embodiments of the invention are directed to systems, methods, and computer program products for an electronic system for management of image processing model database. The system is configured for versioning machine-learning neural-network based image processing models and identifying and tracking mutations in hyper parameters amongst versions of image processing models. The system is configured to determine that a second image processing model is a version of a first image processing model. The system is further configured to map the mutations in hyper parameters between the first plurality of hyper parameters of the first image processing model and the second plurality of hyper parameters associated with the second image processing model.

A robot according to an embodiment of the present disclosure includes a body which is provided with a battery therein, a head connected to a front or an upper side of the body, a mouth formed on one side of the head and include a fixed portion and a rotatable portion disposed below the fixed portion, a mouth driver configured to rotate the rotatable portion in a vertical direction, a biometric information sensor disposed inside the mouth and exposed to the outside during the lower rotation of the rotatable portion, and a processor configured to acquire health state information of a user through the biometric information sensor.

Methods and systems for using a teleoperation system to train a robot to perform tasks using machine learning are described herein. A teleoperation system may be used to record actions of a robot as used by a human teleoperator. The teleoperation system may provide a teleoperator insight into the state of the robot and may provide feedback to the teleoperator allowing the teleoperator to feel what the robot is feeling. For example, sensor information from the robot may be sent to the teleoperation system and output to the teleoperator in various forms including vibrations, video, visual cues, or sound. The teleoperation system may output visual guides to the teleoperator so that the teleoperator may know how to control the robot to complete a task in a desired manner.

The present disclosure discloses a method and an apparatus for driving a digital human, and an electronic device. The method includes obtaining a target action corresponding to a target text; obtaining a reference action to be executed before the digital human executes the target action when the digital human is driven to output speech based on the target text; modifying a target action parameter of the target action according to a reference action parameter of the reference action; and driving the digital human to execute the target action according to a modified target action parameter when driving the digital human to output the speech based on the target text.