An artificial intelligence neural network apparatus, comprising: a labeled learning database having data of a feature vector composed of N elements; a first feature vector image converter configured to visualize the data in the learning database to form an imaged learning feature vector image database; a deep-learned artificial intelligence neural network configured to use a learning feature vector image in the learning feature vector image database to perform an image classification operation; an inputter configured to receive a test image, and generate test data based on the feature vector; and a second feature vector image converter configured to visualize the test data and convert the visualized test data into a test feature vector image. The deep-learned artificial intelligence neural network is configured to determine a class of the test feature vector image.

A device may receive data that includes invoice data related to historical invoices from an organization, contact data related to historical contacts between the organization and various entities, and dispute data related to historical disputes between the organization and the various entities. The device may determine a profile for the data. The device may determine a set of supervised learning models for the historical invoices based on one or more of the historical contacts, the historical disputes, the historical invoices, or historical patterns related to the historical invoices. The device may determine, using the profile, a set of unsupervised learning models for the historical invoices independent of the one or more of the historical contacts, the historical disputes, or the historical patterns. The device may determine, utilizing a super model, a prediction for the invoice after the super model is trained. The device may perform one or more actions.

An example operation includes one or more of detecting a gesture in a transport performed by a transport occupant, determining an occupant status associated with the transport occupant, determining whether the occupant status permits the gesture to be performed, and performing an action associated with the gesture, when the occupant status permits the gesture to be performed.

A recognition processing section performs subject recognition in a processing area of an image obtained by an imaging section. The recognition processing section determines an image characteristic of the processing area on the basis of a characteristic map indicating an image characteristic of the image obtained by the imaging section and uses a recognizer corresponding to the image characteristic of the processing area. The characteristic map includes a map based on an optical characteristic of an imaging lens used in the imaging section and is stored in a characteristic information storage section. An imaging lens has a winder angle of view in all directions or in a predetermined direction than a standard lens, and the optical characteristic thereof differs depending on a position on the lens. The recognition processing section performs the subject recognition using a recognizer corresponding to resolution or skewness of the processing area, for example.

Examples disclosed herein relate to determining a response based on hierarchical models. In one implementation, a processor applies a first model to an image of an environment to select a second model. The processor applies the selected second model to the image and creates an environmental description representation based on the output of the second model. The processor determines a response based on the environmental description information.

An information processing apparatus, comprising: one or more processing devices; and one or more storage devices storing instructions for causing the one or more processing devices to: acquire observation information obtained through observation of a target region from a flying object flying in outer space; classify the target object by inputting the observation information acquired to a classifier so trained as to output a classification result obtained by classifying a target object present in the target region if the observation information is input; accept designation input for designating the target object; and output the observation information including a classification result of the target object designated.

An information processing apparatus comprising: one or more processing devices; and one or more storage devices storing instructions for causing the one or more processing devices to: store observation information obtained through observation of a target region on the earth from a flying object moving in outer space into the one or more storage devices; output to a terminal device a map image corresponding to the target region on which a dividing line for dividing the target region into a plurality of areas is superimposed; accept designation input for designating multiple ones of the plurality of areas divided by the dividing line from the terminal device; extract area observation information being a part of the observation information and corresponding to each of the multiple ones of the plurality of areas designated; and output to the terminal device the area observation information extracted.

This disclosure relates to method and system for classifying an object in input data using an artificial neural network (ANN) model. The method may include extracting positive features and orthogonal features associated with the object in the input data, performing a partial classification of the object based on the positive features by a first part of the ANN model, and determining an accuracy of the classification of the object based on the orthogonal features by a second part of the ANN model. The positive features are features uniquely contributing to identification of a class for the object, while the orthogonal features are features not contributing to identification of the class but contributing to identification of one or more of remaining classes.

A method includes: capturing one or more images of an unorganized collection of items inside a first machine; determining one or more item types of the unorganized collection of items from the one or more images, comprising: dividing a respective image in the one or more images into a respective plurality of sub-regions; performing feature detection on the respective plurality of sub-regions to obtain a respective plurality of regional feature vectors, wherein a regional feature vector for a sub-region indicates characteristics for a plurality of predefined local item features for the sub-region; generating an integrated feature vector by combining the respective plurality of regional feature vectors; and applying a plurality of binary classifiers to the integrated feature vector; and selecting a machine setting for the first machine based on the determined one or more clothes type in the unorganized collection of items.

In some examples, a client accesses an AI-enabled web solution through an edge device. The edge device has one or more locally cached faster first AI models, and is also connected to a remotely stored slower, but more accurate and complex, second AI model. The edge device may execute an inference operation using one of the simpler models, but its result may deviate from that of the complex cloud based model. In embodiments, to improve the accuracy and still obtain the benefit of faster response time from a locally cached model, an intelligent cache decision maker is provided. The cache decision maker includes a third AI model, trained to determine, on a per request basis, whether one of the simpler models at the edge may be used, or whether it is necessary to use the more complex cloud based model to respond to the client request.