A detection and tracking system and method using a camera unit on a robot, or alternatively a camera mounted inside the pool overlooking the bottom of the pool, for safety monitoring for use in and around water-related environments. The robot is able to propel itself and move throughout the body of water, both on the surface and underwater, and the camera unit functions both on the surface and underwater. The robot optimizes the cleaning cycle of the body of water utilizing deep learning techniques. The robot has localization sensors and software that allow the robot to be aware of the robot's position in the pool. The camera is able to send its video feed live over the internet, the processing is performed in the cloud, and the robot sends and receives data from the cloud. The processing utilizes deep learning algorithms, including artificial neural networks, that perform video analytics.

A method for sharing avatars is provided, which includes: receiving, from a first user terminal, an avatar share request to share a first avatar including a first set of avatar components, with a second user terminal; creating a first temporary avatar by copying the first set of avatar components; associating the first temporary avatar with first link information; and transmitting the first link information to the second user terminal.

An example method includes receiving, by a computational assistant executing at one or more processors, a representation of an utterance spoken at a computing device; identifying, based on the utterance, a task to be performed by the computational assistant; responsive to determining, by the computational assistant, that complete performance of the task will take more than a threshold amount of time, outputting, for playback by one or more speakers operably connected to the computing device, synthesized voice data that informs a user of the computing device that complete performance of the task will not be immediate; and performing, by the computational assistant, the task.

Disclosed herein is a software technology for facilitating an interactive conversational session between a user and a digital conversational character. For instance, in one aspect, the disclosed process may involve two primary phases: (1) an authoring phase that involves a first user accessing a content authoring tool to create a given type of visual conversation application that facilitates interactions between a second user and a digital conversational character in an interactive conversational session, and (2) a rendering phase that involves the second user accessing the created visual conversation application to interact with the digital conversational character in an interactive conversational session. In one implementation, accessing the created visual conversation application may involve detecting an object and identifying information associated with the detected object. The digital conversational character involved in the interactive conversational session may be superimposed onto a real-world environment.

Smart livestock management method and system based on Internet of things. The smart livestock management system may include a gate. The gate may include a frame forming a moving path of an animal, an external device disposed in the frame and configured to radiate electromagnetic waves toward the moving path and to receive identification information and bio information of the animal from an implant device inserted into the body of the animal passing through the moving path, and a camera configured to obtain image data of the animal passing through the moving path.

Smart livestock management method and system based on Internet of things. The smart livestock management system may include a gate. The gate may include a frame forming a moving path of an animal, an external device disposed in the frame and configured to radiate electromagnetic waves toward the moving path and to receive identification information and bio information of the animal from an implant device inserted into the body of the animal passing through the moving path, and a camera configured to obtain image data of the animal passing through the moving path.

A method comprises receiving a command inputted to a first device, analyzing the command and identifying a second device based at least in part on the analysis. In the method, the second device is authenticated and a connection is established between the first device and the second device over one or more networks. The command is for execution of a task on the second device.

A pre-training apparatus and method for reinforcement learning based on a Generative Adversarial Network (GAN) is provided. GAN includes a generator and a discriminator. The method comprising receiving training data from a real environment where the training data includes a data slice corresponding to a first state-reward pair and a first state-action pair, training the GAN using the training data, training a relations network to extract a latent relationship of the first state-action pair with the first state-reward pair in a reinforcement learning context, causing the generator trained with training data to generate first synthetic data, processing a portion of the first synthetic data in the relations network to generate a resulting data slice, merging the second state-action pair portion of the first synthetic data with the second state-reward pair from the relations network to generate second synthetic data to update a policy for interaction with the real environment.

A method for training a central artificial intelligence module (“AI module”) for highly or fully automated operation of a vehicle, the central AI module to translate input signals into output signals, and the translation is carried out using a processing chain that is adaptable by modifying values of internal processing parameters, wherein the training of the central AI module takes place by modifying the internal processing parameters based on further internal processing parameters of further AI modules, the further AI modules being in a plurality of vehicles and translating input signals into output signals in each case, and the translations taking place using processing chains that are able to be adapted by modifying values of further internal processing parameters, the further AI modules having been trained using input signals that are based on environment data acquired with using environment sensor systems installed in the vehicles.

Implementations described herein relate to methods, devices, and computer-readable media to perform gating for video analysis. In some implementations, a computer-implemented method includes obtaining a video comprising a plurality of frames and corresponding audio. The method further includes performing sampling to select a subset of the plurality of frames based on a target frame rate and extracting a respective audio spectrogram for each frame in the subset of the plurality of frames. The method further includes reducing resolution of the subset of the plurality of frames. The method further includes applying a machine-learning based gating model to the subset of the plurality of frames and corresponding audio spectrograms and obtaining, as output of the gating model, an indication of whether to analyze the video to add one or more video annotations.