Abstract: In some examples, a server instructs individual software applications to process individual tasks and determines a plurality of outputs resulting from processing. The server determines, based on the plurality of outputs, individual performance scores associated with individual software applications, and determines individual features associated with individual task data of multiple task data. The server receives task data associated with a task, determines at least one feature associated with the task data based on analyzing the task data and predicts, using at least one machine learning model, the individual performance scores associated with the individual software applications that have processed the task. The server selects at least one software application from the plurality of software applications based on an associated performance score, generates a recommendation of the at least one software application, and transmits the recommendation to the user device.

Abstract: A method for facilitating performing of tasks optimally using software applications. The method includes receiving, using a communication device, task data from a user device, analyzing, using a processing device, the task data, extracting, using the processing device, a feature associated with the task data, analyzing, using the processing device, the feature using a machine learning model, training the machine learning model for predicting a performance score associated with each of software applications capable of performing a task, selecting, using the processing device, a software application from the software applications based on the performance score, generating, using the processing device, a recommendation of the software application based on the selecting, and transmitting, using the communication device, the recommendation and the output of the chosen software application based on the task input data, to the user device.

Abstract: A server receives a request including information associated with an objective and generates, using a first machine learning model, an architecture of an artificial intelligence-based solution to address the objective. The server generates the artificial intelligence-based solution based on the architecture by: identifying a second machine learning model in a first database and identifying a third machine learning model in a second database. The second machine learning model is a first portion of the artificial intelligence-based solution and is available via a marketplace. The third machine learning model is a second portion of the artificial intelligence-based solution and is unavailable via the marketplace. The server generates the artificial intelligence-based solution based at least in part on a combination of: the second machine learning model and the third machine learning model. The server enables access to the artificial intelligence-based solution via the marketplace.

Abstract: A method for facilitating performing of tasks optimally using software applications. The method includes receiving, using a communication device, task data from a user device, analyzing, using a processing device, the task data, extracting, using the processing device, a feature associated with the task data, analyzing, using the processing device, the feature using a machine learning model, training the machine learning model for predicting a performance score associated with each of software applications capable of performing a task, selecting, using the processing device, a software application from the software applications based on the performance score, generating, using the processing device, a recommendation of the software application based on the selecting, and transmitting, using the communication device, the recommendation and the output of the chosen software application based on the task input data, to the user device.

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: 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 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: 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 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 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 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: 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.