Abstract: Performing collaborative search engine searching. The method includes receiving user input at a user interface for performing a plurality searches on a first search engine. The method further includes receiving user input at the user interface applying one or more augmentation AI models to searches in the plurality of searches. The method further includes creating a shareable, executable package executable by one or more search engines based on the plurality of searches and the applied AI models that when executed by the search engines causes the search engines to apply the AI models to searches performed at the search engines.

Abstract: Expanding search engine functionality using AI models. A method includes, as part of a search session, receiving user input at a search engine. One or more searches on a set of data using the user input. Search results are provided from the one or more searches to a user. Based on a history of the search session, suggestions are provided in a user interface of AI models that could be applied to expand potential search results for the search session. User input is received at the user interface selecting one or more of the suggested AI model. The one or more selected AI models are applied to expand the set of data. Search results to the user based on searching the expanded set of data.

Abstract: The improved exercise of artificial intelligence by providing a systematic way for a computing system to interface with output from AI models. To do this, the computing system obtains results of an input data set being applied to an AI model. The results are then refined based upon characteristic(s) of the AI model and perhaps the input data set. Based upon characteristic(s) of the AI model and perhaps the input data set, interface element(s) are identified that can be used to interface with the refined results. The interface element(s) are then communicated to an interface element that interfaces with the refined results. The interface element(s) may include, for instance, operator(s) or term(s) that may be used to query against the refined results and/or an identification of visualization(s) that may be used to present to a user results of queries against the refined results.

Abstract: The improved exercise of artificial intelligence. Raw output data is obtained by applying an input data set to an artificial intelligence (AI). Such raw output data is sometimes difficult to interpret. The principles defined herein provide a systematic way to refine the output for a wide variety of AI models. An AI model collection characterization structure is utilized for purpose of refining AI model output so as to be more useful. The characterization structure represents, for each of multiple and perhaps numerous AI models, a refinement of output data that resulted from application of an AI model to input data. Upon obtaining output data from the AI model, the appropriate refinement may then be applied. The refined data may then be semantically indexed to provide a semantic index. The characterization structure may also provide tailored information to allow for intuitive querying against the semantic index.

Abstract: The improved exercise of artificial intelligence by systematically refining and semantically indexing the output from AI models, so that the semantic index is highly relevant. To do this, the computing system obtains results of an input data set being applied to an AI model. The computing system then determines a refinement to apply to the obtained results. This determination may be based on one or more characteristics of the AI model and/or input data set. The determination may also be based on hints associated with that AI model, and/or learned behavior regarding how that AI model is typically used. The obtained results are then refined using the determined refinement. It is then this more relevant refined results that are semantically indexed to generate the semantic index. Thus, the semantic index represents, the more useful output from an AI model, which is semantically exposed so as to provide meaning.

Abstract: Applying AI models to a search using a search engine for a user. A method includes receiving user search input at a search engine user interface. The search input is used with the search engine to obtain first search results. One or more AI models are applied to the first search results to obtain additional search data. The additional search data is searched to identify additional search results. Using the additional search results, a subset of second search results are identified from the first search results while filtering out other search results from the first search results. At least a portion of the second search results are provided to the user in the user interface while preventing the other search results that were filtered from being displayed in the user interface, such that a user at the user interface has the second search results returned as results.

Abstract: Expanding search engine functionality using AI models. A method includes, as part of a search session, receiving user input at a search engine. One or more searches on a set of data using the user input. Search results are provided from the one or more searches to a user. Based on a history of the search session, suggestions are provided in a user interface of AI models that could be applied to expand potential search results for the search session. User input is received at the user interface selecting one or more of the suggested AI model. The one or more selected AI models are applied to expand the set of data. Search results to the user based on searching the expanded set of data.

Abstract: Performing collaborative search engine searching. The method includes receiving user input at a user interface for performing a plurality searches on a first search engine. The method further includes receiving user input at the user interface applying one or more augmentation AI models to searches in the plurality of searches. The method further includes creating a shareable, executable package executable by one or more search engines based on the plurality of searches and the applied AI models that when executed by the search engines causes the search engines to apply the AI models to searches performed at the search engines.

Abstract: The improved exercise of artificial intelligence. Raw output data is obtained by applying an input data set to an artificial intelligence (AI). Such raw output data is sometimes difficult to interpret. The principles defined herein provide a systematic way to refine the output for a wide variety of AI models. An AI model collection characterization structure is utilized for purpose of refining AI model output so as to be more useful. The characterization structure represents, for each of multiple and perhaps numerous AI models, a refinement of output data that resulted from application of an AI model to input data. Upon obtaining output data from the AI model, the appropriate refinement may then be applied. The refined data may then be semantically indexed to provide a semantic index. The characterization structure may also provide tailored information to allow for intuitive querying against the semantic index.

Abstract: Providing an improved user interface to a user for facilitating data management produced by artificial intelligence. A method includes receiving user input adding an input dataset to an active area of a user interface. The method further includes receiving user input adding an artificial intelligence model to the active area of the user interface. The method further includes, based on the user adding the artificial intelligence model to the active area of the user interface, providing feedback on the user interface to the user indicating an effect of adding the artificial intelligence model to the active area of the user interface.

Abstract: The improved exercise of artificial intelligence by systematically refining and semantically indexing the output from AI models, so that the semantic index is highly relevant. To do this, the computing system obtains results of an input data set being applied to an AI model. The computing system then determines a refinement to apply to the obtained results. This determination may be based on one or more characteristics of the AI model and/or input data set. The determination may also be based on hints associated with that AI model, and/or learned behavior regarding how that AI model is typically used. The obtained results are then refined using the determined refinement. It is then this more relevant refined results that are semantically indexed to generate the semantic index. Thus, the semantic index represents, the more useful output from an AI model, which is semantically exposed so as to provide meaning.

Abstract: The improved exercise of artificial intelligence by providing a systematic way fora computing system to interface with output from AI models. To do this, the computing system obtains results of an input data set being applied to an AI model. The results are then refined based characteristic(s) of the AI model and perhaps the input data set. Based upon characteristic(s) of the AI model and perhaps the input data set, interface element(s) are identified that can be used to interface with the refined results. The interface element(s) are then communicated to an interface element that interfaces with the refined results. The interface element(s) may include, for instance, operator(s) or term(s) that may be used to query against the refined results and/or an identification of visualization(s) that may be used to present to a user results of queries against the refined results.

Abstract: A uniform approach for web service discovery is provided through common auto-discovery logic for cloud-based or enterprise-based deployments for a specific web service. Appropriate credentials may be applied employing a uniform method using an appropriate authentication scheme for on-premise, fully hosted, or partially hosted auto-discovered web services. A common interface may be implemented even if the protocols for discovery differ from service to service or between cloud-based deployment and enterprise-based deployment for the same service. Web service locations auto-discovered through different protocols may be cached for subsequent use.

Abstract: Techniques to distribute messages using communication pipelines are described. An apparatus may comprise a message receiving component operative to receive a message at a first server for forwarding to a virtual receiver, a routing component operative to identify a second server maintaining the virtual receiver and determine a communication pipeline between the first server and the second server, and a message sending component operative to send the message from the first server to the second server over the communication pipeline. Other embodiments are described and claimed.

Abstract: A pluggable token provider model for message level authentication across multiple web services is provided. Web service and token provider implementations within a client application are separated from an actual component that operates the business logic to formulate and understand a web request. The web service components may request web services to be executed and supply the body for the web service message while a common framework maintains the web services metadata, which includes definitions associated with respective tokens. The framework may further maintain token provider implementations that actually fetch authentication tokens and perform the web requests.

Abstract: Techniques to distribute messages using communication pipelines are described. An apparatus may comprise a message receiving component operative to receive a message at a first server for forwarding to a virtual receiver, a routing component operative to identify a second server maintaining the virtual receiver and determine a communication pipeline between the first server and the second server, and a message sending component operative to send the message from the first server to the second server over the communication pipeline. Other embodiments are described and claimed.

Abstract: A pluggable token provider model for message level authentication across multiple web services is provided. Web service and token provider implementations within a client application are separated from an actual component that operates the business logic to formulate and understand a web request. The web service components may request web services to be executed and supply the body for the web service message while a common framework maintains the web services metadata, which includes definitions associated with respective tokens. The framework may further maintain token provider implementations that actually fetch authentication tokens and perform the web requests.

Abstract: A uniform approach for web service discovery is provided through common auto-discovery logic for cloud-based or enterprise-based deployments for a specific web service. Appropriate credentials may be applied employing a uniform method using an appropriate authentication scheme for on-premise, fully hosted, or partially hosted auto-discovered web services. A common interface may be implemented even if the protocols for discovery differ from service to service or between cloud-based deployment and enterprise-based deployment for the same service. Web service locations auto-discovered through different protocols may be cached for subsequent use.