Abstract: In accordance with the present disclosure, one or more computing devices and/or methods are provided. In an example, a first group of content items may be identified. A content interface may be provided for display on a client device. The content interface may comprise a first selectable input for accessing a first content item of the first group of content items, a second selectable input for accessing a second content item of the first group of content items, and/or a question and answer interface. A query may be received via the question and answer interface. Using a generative AI tool, a response to the query may be generated based upon the first group of content items. A representation of the response to the query may be displayed via the question and answer interface.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. Machine learning is performed based on training data via a dual loop learning process that includes a first loop for data decoding learning and a second loop for label decoding learning. In the first loop, first parameters associated with decoding are updated to generate updated first parameters based on a first label, estimated via the decoding using the first parameters, and a second label, predicted via the label decoding using second parameters. In the second loop, the second parameters associated with the label decoding are updated to generate updated second parameters based on a third label, obtained via the decoding using the updated first parameters, and a ground truth label.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. Machine learning is performed based on training data via a dual loop learning process that includes a first loop for data decoding learning and a second loop for label decoding learning. In the first loop, first parameters associated with decoding are updated to generate updated first parameters based on a first label, estimated via the decoding using the first parameters, and a second label, predicted via the label decoding using second parameters. In the second loop, the second parameters associated with the label decoding are updated to generate updated second parameters based on a third label, obtained via the decoding using the updated first parameters, and a ground truth label.

Abstract: In some aspects, the techniques described herein relate to a method including: receiving, at a processor, an uncrawled URL corresponding to a webpage; applying, by the processor, a webpage classification model to the uncrawled URL to determine a probability for a plurality of webpage classifications; assigning, by the processor, one or more labels to the uncrawled URL corresponding to one or more classifications of the plurality of webpage classifications that meet a threshold; and providing, by the processor, a content item to be displayed on the webpage based on the one or more labels.

Abstract: The present teaching relates to method, system, medium, and implementations for machine learning. Machine learning is performed based on training data via a dual loop learning process that includes a first loop for data decoding learning and a second loop for label decoding learning. In the first loop, first parameters associated with decoding are updated to generate updated first parameters based on a first label, estimated via the decoding using the first parameters, and a second label, predicted via the label decoding using second parameters. In the second loop, the second parameters associated with the label decoding are updated to generate updated second parameters based on a third label, obtained via the decoding using the updated first parameters, and a ground truth label.