An information processing apparatus includes a hardware processor which (i) performs learning by a learning data set associated with a correct answer label for a preset problem and creates a machine learning model for estimating a correct answer to the preset problem for input data, (ii) estimates the correct answer to the preset problem for the input data by using the machine learning model, (iii) in response to a user operation, determines a label indicating a result of the estimation as a correct answer label of the input data or corrects the label to determine the corrected label as a correct answer label of the input data, and (iv) additionally registers the determined correct answer label as learning data in association with the input data in the learning data set.

Systems and method for assessing annotators by way of annotated images annotated by said annotators. Agent or annotator model modules are trained using annotated images annotated by specific annotators. A baseline model module is also trained using all of the annotated images used in training the agent model modules. The trained agent model modules are then used to annotate an evaluation dataset to result in evaluation result annotated images. The trained baseline model module is also used to annotate the evaluation dataset to result in its own evaluation result annotated images. The evaluation results from the agent model modules are compared with the evaluation result from the baseline model module. Based on the comparison results, scores are allocated to each agent model module. The scores are used to group agent model modules and annotators that correspond to the low scoring agent model modules can be targeted for retraining.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for semi-supervised training of an object recognition model. The methods, systems, and apparatus include a monitoring system including a camera located at a property and configured to generate images and one or more computers and one or more storage devices storing instructions that are operable, when executed by the one or more computers, to cause the one or more computers to perform actions of determining a cluster of images meets a threshold for number of included images and a threshold for cluster tightness. A representative image of the cluster is selected and a query including the representative image of the cluster is provided. User feedback responsive to the query is received and an object recognition model is updated based on the user feedback.

Systems and methods are described to systems and methods for training a machine learning model to categorize each pixel of an input overhead image using received overhead images, and using a trained machine learning model to determine, for each pixel of input overhead images, to which land use or land cover mapping category each pixel of each overhead image belongs. The provided systems and methods may generate a map of a geographic area associated with the plurality of overhead images based on the plurality of overhead images and on the determined categories.

A hyperspectral stereoscopic CubeSat with computer vision and artificial intelligence capabilities consists of a device and a data processing methodology. The device comprises a number of VIS-NIR-TIR hyperspectral sensors, a central processor with memory, a supervisor system running independently of the imager system, radios, a solar panel and battery system, and an active attitude control system. The device is launched into low earth orbit to capture, process, and transmit stereoscopic hyperspectral imagery in the visible and infrared portions of the electromagnetic spectrum. The processing methodology therein comprises computer vision and convolutional neural network algorithms to perform spectral feature identification and data transformations.

A method is provided for training at least one classifier model used by an artificial intelligence (AI) system to recognize each of a set of objects and to assign each of the set of objects to a class. The method includes training the at least one classifier model on a training dataset, thereby producing at least one trained classifier model; using the at least one trained classifier model to detect and classify each member of a set of objects, thereby generating a set of inferences, wherein each inference includes (a) a cropped image of a classified object, (b) the classified object's inferred class, and (c) a confidence score associated with the inferred classification; examining the set of inferences with a machine implemented audit trigger, wherein the audit trigger identifies a subset of the set of inferences whose members have (i) a confidence score that falls below a predetermined threshold value, or (ii) a missing classification; and if the identified subset has at least one member, subjecting the identified subset to a human audit, thereby yielding a corrected set of observations, wherein, for each member of the corrected set of observations, the inferred class of the corresponding member of the set of inferences is replaced with a corrected class. The corrected set of observations is then added to a training dataset and used to improve the future accuracy of the classifier model.

One or more multi-stage optimization iterations are performed with respect to a compression algorithm. A given iteration comprises a first stage in which hyper-parameters of a perceptual quality algorithm are tuned independently of the compression algorithm. A second stage of the iteration comprises tuning hyper-parameters of the compression algorithm using a set of perceptual quality scores generated by the tuned perceptual quality algorithm. The final stage of the iteration comprises performing a compression quality evaluation test on the tuned compression algorithm.

This patent includes a method for displaying a 2D image on a non-planar display with a top portion of said non-planar display curving inwards towards a user's viewing position, a bottom portion of said non-planar display curves inwards towards said user's viewing position, a left portion of said non-planar display curves inward towards said user's viewing position and a right portion of said non-planar display curves inward towards said user's viewing position. This type of display can be used as a virtual display for extended reality head display units including virtual reality, augmented reality or mixed reality displays. For a virtual display, advanced features can be performed including stereoscopic viewing the screen with a convergence point shifting technique, zooming and rotating to maximize usability. The virtual display can be modified in its horizontal curvature or vertical curvature per user preference. The non-planar display can be a tangible TV, monitor, phone or tablet as discussed in this patent.

An artificial intelligence (AI) system that utilizes a machine learning algorithm, such as deep learning, etc. and an application of the AI system is provided. A method, performed by a server, of integrating and managing a plurality of databases (DBs) includes obtaining a plurality of knowledge graphs related to DBs generated from the plurality of DBs having different structures from one another, inputting the plurality of knowledge graphs related to DBs into a learning model related to DB for determining a correlation between data in the plurality of DBs, and obtaining a virtual integrated knowledge graph output from the learning model related to DB and including information about a correlation extracted from the plurality of knowledge graphs related to DBs.

Techniques to mimic a target food item using artificial intelligence are disclosed. A formula generator is trained using ingredients and using recipes and, given a target food item, determines a formula that matches the given target food item. A flavor generator is trained using recipes and their associated flavor information and, given a formula, the flavor generator determines a flavor profile for the given formula. The flavor profile may be used to assist the formula generator in generating a subsequent formula. A recipe generator is trained using recipes and, given a formula, determines a cooking process for the given formula. A food item may be cooked according to a recipe, and feedback, including a flavor profile, may be provided for the cooked food item. The recipe and its feedback may be added to a training set for the flavor generator.