System and method for training a human perception predictor to determine level of perceived similarity between data samples, the method including: receiving at least one media file, determining at least one identification region for each media file, applying at least one transformation on each identification region for each media file until at least one modified media file is created, receiving input regarding similarity between each modified media file and the corresponding received media file, and training a machine learning model with an objective function configured to predict similarity between media files by a human observer in accordance with the received input.

An embodiment of the invention may include a method, computer program product, and system for generating ground truth data for a plurality of cognitive capabilities within an overall cognitive system. The embodiment may include configuring multiple sets of training data. Each set of training data corresponds to a separate cognitive capability. The embodiment may include displaying a set of ground truth curation activities via a user interface. The embodiment may include determining the ground truth curation activities performed for a first type of data for a first duration. The first type of data is selected from the single set of grouped training data. The embodiment may include determining whether the first duration has exceeded a pre-determined threshold. The embodiment may include switching the curation activities to a second type of data. The second type of data is selected from the single set of grouped data.

A method for determining a condition of an object, in particular whether the object is a normal condition or an abnormal condition. The method includes processing data of an object based on a determination model to determine a condition of the object. The method also includes classifying the object as in a normal condition or an abnormal condition based on the processing. The abnormal condition may indicate that the object is defective.

A machine learning method is used for improving accuracy of an automated optical inspection (AOI) device. The method includes obtaining an image of a component to be inspected, processing the image to generate digital image information, establishing a machine learning model according to the digital image information, inputting the digital image information into the machine learning model for determination, verifying accuracy of a result of determination by the machine learning model, adjusting and optimizing the machine learning model according to the result of determination of the accuracy of the machine learning model, and improving the machine learning model until the machine learning model reaches a predetermined accuracy.

Methods and apparatus for the application of machine learning to radiographic images of animals. In one embodiment, the method includes receiving a set of radiographic images captured of an animal, applying one or more transformations to the set of radiographic images to create a modified set, segmenting the modified set using one or more segmentation artificial intelligence engines to create a set of segmented radiographic images, feeding the set of segmented radiographic images to respective ones of a plurality of classification artificial intelligence engines, outputting results from the plurality of classification artificial intelligence engines for the set of segmented radiographic images to an output decision engine, and adding the set of segmented radiographic images and the output results from the plurality of classification artificial intelligence engines to a training set for one or more of the plurality of classification artificial intelligence engines. Computer-readable apparatus and computing systems are also disclosed.

In one embodiment, a device obtains perception results generated by one or more perception models that use media data as input that is transmitted between endpoints of an online application via a network. The device computes performance measures for the one or more perception models, based in part on the perception results and on the media data. The device quantifies, based on the performance measures, quality of experience for the online application. The device causes a configuration change to be made with respect to the online application, based on the quality of experience.

A computer-implemented method for classification of data by a machine learning system using a logic constraint for reducing a data labeling requirement. The computer-implemented method includes: generating a first embedding space from a first partially labeled training data set, wherein in the first embedding space, content-wise related training data of the first partially labeled training data are clustered together, determining at least two clusters in the first embedding space formed from the first partially labeled training data, and training a machine learning model based, at least in part, on a second partially labeled training data set and the at least two clusters, wherein the at least two clusters are used as training constraints.

An information processing method includes: obtaining noise region estimation information output from a first converter by a first image including a noise region being input to the first converter; obtaining a second image, on which noise region removal processing has been performed, output from a second converter by the noise region estimation information and the first image being input to the second converter; generating a fourth image including the estimated noise region by using the noise region estimation information and a third image including no noise region and a scene corresponding to the first image; training the first converter by using machine learning in which the first image is reference data and the fourth image is conversion data; and training the second converter by using machine learning in which the third image is reference data and the second image is conversion data.

Provided are a computer program product, system, and method for determining content values to render in a computer user interface based on user feedback and information. Detection is made of a section of the document rendered in a computer user interface that the user is observing. A monitoring device detects user biometric data in response to detecting the section the user is observing. Input is provided to a machine learning module comprising the content value in the section the user is observing, the user biometric data, and personal information of the user. Output from the machine learning module indicates a likelihood that the user approved or disapproved of the content value in the section the user was observing. The output is used to determine whether to send a substitute content value of the plurality of content values to render in the section the user is observing.

Systems and methods for improving the accuracy of a computer system for object identification/classification through the use of weakly supervised learning are provided herein. In some embodiments, the method includes (a) receiving at least one set of curated data, wherein the curated data includes labeled images, (b) using the curated data to train a deep network model for identifying objects within images, wherein the trained deep network model has a first accuracy level for identifying objects, receiving a first target accuracy level for object identification of the deep network model, determining, automatically via the computer system, an amount of weakly labeled data needed to train the deep network model to achieve the first target accuracy level, and augmenting the deep network model using weakly supervised learning and the weakly labeled data to achieve the first target accuracy level for object identification by the deep network model.