Abstract: The present disclosure relates to systems, methods, and computer readable media that evaluate performance of a machine learning system in connection with a test dataset. For example, systems disclosed herein may receive a test dataset and identify label information for the test dataset including feature information and ground truth data. The systems disclosed herein can compare the ground truth data and outputs generated by a machine learning system to evaluate performance of the machine learning system with respect to the test dataset. The systems disclosed herein may further generate feature clusters based on failed outputs and corresponding features and generate a number of performance views that illustrate performance of the machine learning system with respect to clustered groupings of the test dataset.

Abstract: Biases in search and retrieval (i.e., situations where searchers seek or are presented with information that significantly deviates from the truth) may be detected by comparison to one or more authoritative sources. Once bias or potential bias is detected, techniques may be applied to indicate and/or compensate for the bias. Such techniques may allow users to more easily assess the veracity of search results, and increase the chances that users will locate accurate answers to their questions.

Abstract: Biases in search and retrieval (i.e., situations where searchers seek or are presented with information that significantly deviates from the truth) may be detected by comparison to one or more authoritative sources. Once bias or potential bias is detected, techniques may be applied to indicate and/or compensate for the bias. Such techniques may allow users to more easily assess the veracity of search results, and increase the chances that users will locate accurate answers to their questions.

Abstract: The present disclosure relates to systems, methods and computer readable media for evaluating performance of a machine learning system and providing one or more performance views representative of the determined performance. For example, systems disclosed herein may receive or identify performance information including outputs, accuracy data, and feature data associated with a plurality of test instances. In addition, systems disclosed herein may provide one or more performance views via a graphical user interface including graphical elements (e.g., interactive elements) and indications of accuracy data and other performance data with respect to feature clusters associated with select groupings of test instances from the plurality of test instances. The performance views may include interactive features to enable a user to view and intuitively understand performance of the machine learning system with respect to clustered groupings of test instances that share common characteristics.

Abstract: The present disclosure relates to systems, methods, and computer readable media that evaluate performance of a machine learning system in connection with a test dataset. For example, systems disclosed herein may receive a test dataset and identify label information for the test dataset including feature information and ground truth data. The systems disclosed herein can compare the ground truth data and outputs generated by a machine learning system to evaluate performance of the machine learning system with respect to the test dataset. The systems disclosed herein may further generate feature clusters based on failed outputs and corresponding features and generate a number of performance views that illustrate performance of the machine learning system with respect to clustered groupings of the test dataset.

Abstract: An expected value of a measurement in a first context may be inferred based at least partly on a contextual signal. The contextual signal may comprise an actual value that is: (i) of a same type as the expected value, and (ii) associated with a second context that is different from the first context (e.g., the contexts can comprise geographical areas), or the contextual signal may comprise an actual value that is: (i) of a different type than a type of the expected value, and (ii) associated with the first context, or a second context that is different from the first context. If a difference between the expected value and an actual value of the first context is greater than a threshold difference, this condition is considered an anomaly. A detected anomaly may be used to determine an event that may be significant or otherwise of interest to a user community.

Abstract: A system that analyses content of electronic communications may automatically extract requests or commitments from the electronic communications. In one example process, a processing component may analyze the content to determine one or more meanings of the content; query content of one or more data sources that is related to the electronic communications; and based, at least in part, on (i) the one or more meanings of the content and (ii) the content of the one or more data sources, automatically identify and extract a request or commitment from the content. Multiple actions may follow from initial recognition and extraction, including confirmation and refinement of the description of the request or commitment, and actions that assist one or more of the senders, recipients, or others to track and address the request or commitment, including the creation of additional messages, reminders, appointments, or to-do lists.

Abstract: Techniques provide time-aware ranking, such as ranking of information, files or URL (uniform resource locator) links. For example, time-aware modeling assists in determining user intent of a query to a search engine. In response to the query, results are ranked in a time-aware manner to better match the user intent. The ranking may model query, URL and query-URL pair behavior over time to create time-aware query, URL and query-URL pair models, respectively. Such models may predict behavior of a query-URL pair, such as frequency and timing of clicks to the URL of the pair when the query of the pair is posed to the search engine. Results of a query may be ranked by predicted query-URL behavior. Once ranked, the results may be sent to the user in response to the query.

Abstract: Automatic extraction and application of conditional tasks from content is provided. A conditional task system includes a classifier that is trained and used to identify conditional tasks and to learn appropriate times and methods to engage a user for reminding the user about conditional tasks. The conditional task system includes components for enabling an automated detection of a conditional task, extracting of attributes that characterize a condition associated with the task, using information about the condition to determine how to monitor for satisfaction of the condition, determining when and how to engage the user about the task, and notifying the user at an appropriate time and using an appropriate method when the condition is satisfied.

Abstract: Biases in search and retrieval (i.e., situations where searchers seek or are presented with information that significantly deviates from the truth) may be detected by comparison to one or more authoritative sources. Once bias or potential bias is detected, techniques may be applied to indicate and/or compensate for the bias. Such techniques may allow users to more easily assess the veracity of search results, and increase the chances that users will locate accurate answers to their questions.

Abstract: Biases in search and retrieval (i.e., situations where searchers seek or are presented with information that significantly deviates from the truth) may be detected by comparison to one or more authoritative sources. Once bias or potential bias is detected, techniques may be applied to indicate and/or compensate for the bias. Such techniques may allow users to more easily assess the veracity of search results, and increase the chances that users will locate accurate answers to their questions.

Abstract: Biases in search and retrieval (i.e., situations where searchers seek or are presented with information that significantly deviates from the truth) may be detected by comparison to one or more authoritative sources. Once bias or potential bias is detected, techniques may be applied to indicate and/or compensate for the bias. Such techniques may allow users to more easily assess the veracity of search results, and increase the chances that users will locate accurate answers to their questions.

Abstract: An expected value of a measurement in a first context may be inferred based at least partly on a contextual signal. The contextual signal may comprise an actual value that is: (i) of a same type as the expected value, and (ii) associated with a second context that is different from the first context (e.g., the contexts can comprise geographical areas), or the contextual signal may comprise an actual value that is: (i) of a different type than a type of the expected value, and (ii) associated with the first context, or a second context that is different from the first context. If a difference between the expected value and an actual value of the first context is greater than a threshold difference, this condition is considered an anomaly. A detected anomaly may be used to determine an event that may be significant or otherwise of interest to a user community.

Abstract: Wireless networks may be dynamically reconfigured based at least in part on predicted future user device locations. The predicted future user device locations may be used to, for example, to offload user devices to small cells or WiFi networks. The predicted future user device locations may additionally or alternatively be used for targeting directional signals and/or for beam forming for multi-user multi-input/multi-output systems.

Abstract: A system that analyses content of electronic communications may automatically detect requests or commitments from the electronic communications. In one example process, a processor may identify a request or a commitment in the content of the electronic message; based, at least in part, on the request or the commitment, determine an informal contract; and execute one or more actions to manage the informal contract, the one or more actions based, at least in part, on the request or the commitment.

Abstract: A system that analyses content of electronic communications may automatically extract requests or commitments from the electronic communications. In one example process, a processing component may analyze the content to determine one or more meanings of the content; query content of one or more data sources that is related to the electronic communications; and based, at least in part, on (i) the one or more meanings of the content and (ii) the content of the one or more data sources, automatically identify and extract a request or commitment from the content. Multiple actions may follow from initial recognition and extraction, including confirmation and refinement of the description of the request or commitment, and actions that assist one or more of the senders, recipients, or others to track and address the request or commitment, including the creation of additional messages, reminders, appointments, or to-do lists.

Abstract: Biases in search and retrieval (i.e., situations where searchers seek or are presented with information that significantly deviates from the truth) may be detected by comparison to one or more authoritative sources. Once bias or potential bias is detected, techniques may be applied to indicate and/or compensate for the bias. Such techniques may allow users to more easily assess the veracity of search results, and increase the chances that users will locate accurate answers to their questions.

Abstract: Biases in search and retrieval (i.e., situations where searchers seek or are presented with information that significantly deviates from the truth) may be detected by comparison to one or more authoritative sources. Once bias or potential bias is detected, techniques may be applied to indicate and/or compensate for the bias. Such techniques may allow users to more easily assess the veracity of search results, and increase the chances that users will locate accurate answers to their questions.

Abstract: Techniques provide time-aware ranking, such as ranking of information, files or URL (uniform resource locator) links. For example, time-aware modeling assists in determining user intent of a query to a search engine. In response to the query, results are ranked in a time-aware manner to better match the user intent. The ranking may model query, URL and query-URL pair behavior over time to create time-aware query, URL and query-URL pair models, respectively. Such models may predict behavior of a query-URL pair, such as frequency and timing of clicks to the URL of the pair when the query of the pair is posed to the search engine. Results of a query may be ranked by predicted query-URL behavior. Once ranked, the results may be sent to the user in response to the query.

Abstract: Systems and techniques for facilitating and backing the surfacing of predicted commands within a user interface are disclosed. Commands to surface for an active user in productivity applications can be predicted using a personalized community model. The personalized community model is generated using a record of past actions the active user has taken along with the past actions of many users of the productivity application. The actions of the active user within the productivity application are monitored and used to select commands to surface.