Abstract: A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform. The method further includes receiving information associated with a requested operator, and generating, by a processing device executing a first machine learning model, a skeleton architecture of an AI-based solution to the operator based on the information.

Abstract: In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature.

Abstract: In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature.

Abstract: Techniques are described for a contextual natural language understanding (cNLU) framework that is able to incorporate contextual signals of variable history length to perform joint intent classification (IC) and slot labeling (SL) tasks. A user utterance provided by a user within a multi-turn chat dialog between the user and a conversational agent is received. The user utterance and contextual information associated with one or more previous turns of the multi-turn chat dialog is provided to a machine learning (ML) model. An intent classification and one or more slot labels for the user utterance are then obtained from the ML model. The cNLU framework described herein thus uses, in addition to a current utterance itself, various contextual signals as input to a model to generate IC and SL predictions for each utterance of a multi-turn chat dialog.

Abstract: According to various embodiments, the Query Context Translation Engine identifies a topic of a search query history received during a current user session. The search query history in a first language. The Query Context Translation Engine identifies, in a translation table, target text that corresponds with a query in the search query history, the target text comprising at least one word. The Query Context Translation Engine obtains at least one search result based on a translation of the target text in a second language.

Abstract: A method of propagating annotations of content items to a search query is disclosed. A strength of a correspondence between a search query and a listing of an item on a network-based publication system is determined. The strength of the correspondence is based on an analysis of a set of actions by a set of users who submitted the search query. A set of annotations is generated. The set of annotations is propagated to a search engine and used to enhance search results.

Abstract: Techniques for the generation of dubbed audio for an audio/video are described.

Abstract: In various example embodiments, a system and method for a Listing Engine that translates a first listing from a first language to a second language. The first listing includes an image(s) of a first item. The Listing Engine provides as input to an encoded neural network model a portion(s) of a translated first listing and a portions(s) of a second listing in the second language. The second listing includes an image(s) of a second item. The Listing Engine receives from the encoded neural network model a first feature vector for the translated first listing and a second feature vector for the second listing. The first and the second feature vectors both include at least one type of image signature feature and at least one type of listing text-based feature.

Abstract: In one embodiment, a video analytics system includes a camera network for capturing one or more videos and an EVA (electronic video analytics) platform, coupled to the camera network, operable to perform one or more of video aggregation, encryption, storage and analysis. The EVA platform has a content store for storing videos of the one or more captured videos, an event detection engine for defining one or more events that are each assigned a unique key when encountered in a video of the one or more captured videos, an aggregator for aggregating event-containing videos, and a renderer for rendering the event-containing videos.

Abstract: A method receiving a dataset, storing the dataset in a secured drive, synthesizing a representative dataset, in the secured drive, based on the dataset, granting access to a specialist to view the representative dataset, receiving a model that was generated using the representative dataset, running the model on the representative dataset, validating the results of running the model on the representative dataset, and presenting the validated results of running the model on the representative dataset in a graphical user interface.

Abstract: A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform. The method further includes receiving information associated with a requested operator, and generating, by a processing device executing a first machine learning model, a skeleton architecture of an AI-based solution to the operator based on the information.

Abstract: A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform.

Abstract: A method includes using a generator to generate a first result, providing the first result to a critic, generating a first surprise factor based on providing the first result to the critic, based on the first surprise factor, using the generator to generate a second result, providing the second result to the critic, generating a second surprise factor based on providing the second result to the critic, based on the second surprise factor, determining that the generator has generated a most surprising result, and presenting the most surprising result in a graphical user interface.

Abstract: A method includes receiving information associated with a requested operator. The method further includes, in response to receiving the information, generating, by a processing device executing a machine learning model, an artificial intelligence (AI)-based solution to the requested operator, wherein the AI-based solution comprises a plurality of machine-learning models. The method further includes displaying an option to access the AI-based solution in a marketplace platform. The method also includes receiving information associated with a requested operator and identifying, by a processing device executing a first machine learning model, a second machine learning model corresponding to an AI-based solution to the operator.

Abstract: In a flow of computer actions, a computer system (110) receives a request involving a machine translation. In performing the translation (160, 238), or in using the translation in subsequent computer operations (242, 1110), the computer system takes into account known statistical relationships (310), obtained from previously accumulated click-through data (180), between a machine translation performed in a flow, the flow's portions preceding the translation, and success indicators pertaining to the flow's portion following the translation. The statistical relationships are derived by data mining of the click-through data. Further, normal actions can be suspended to use a random option to accumulate the click-through data and/or perform statistical AB testing. Other features are also provided.

Abstract: A mobile device application prompts and conducts audio and/or video tests using a microphone on a smartphone, tablet or laptop in order to record and analyze a patient's speech, cough, breathing and other sounds in order to diagnose the patient with Covid 19, another ailment, or as having normal ranges not indicative of disease. The mobile device's tests and protocols use program instructions, AI processing and other automated tools to facilitate the speed and reliability of the testing.

Abstract: A semantic analysis can be performed to determine an intent of a received query. The intent can relate to a primary object of the query, which can be identified through the semantic analysis. Other attributes can be determined from the query that help to focus the object of the intent. A query vector is generated, based on the intent and primary object, and used to search a multi-dimensional semantic space including semantic representations of possible matches. The attributes are used to adjust the query vector in the semantic space. Objects having vectors ending proximate the query vector are identified as potential search results, with the distance from the query vector being used as a ranking mechanism. If refinement is needed, a dialog is used to obtain additional information from the user. Once results are obtained with sufficient confidence, results can be returned as search results for the query.

Abstract: In a flow of computer actions, a computer system (110) receives a request involving a machine translation. In performing the translation (160, 238), or in using the translation in subsequent computer operations (242, 1110), the computer system takes into account known statistical relationships (310), obtained from previously accumulated click-through data (180), between a machine translation performed in a flow, the flow's portions preceding the translation, and success indicators pertaining to the flow's portion following the translation. The statistical relationships are derived by data mining of the click-through data. Further, normal actions can be suspended to use a random option to accumulate the click-through data and/or perform statistical AB testing. Other features are also provided.

Abstract: A method of propagating annotations of content items to a search query is disclosed. A strength of a correspondence between a search query and a listing of an item on a network-based publication system is determined. The strength of the correspondence is based on an analysis of a set of actions by a set of users who submitted the search query. A set of annotations is generated. The set of annotations is propagated to a search engine and used to enhance search results.

Abstract: Information is provided a user of a mobile device. Media content is sensed by the mobile device and sent to an information processing server. The information processing server also obtains media content from another source and associates at least a portion of the obtained media content with a product or service offer. The sensed media content is correlated to the obtained media content such that an associated buy or service offer is selected. The buy or service offer is sent to the mobile device for display to the user.