Abstract: An artificial intelligence (AI)-based knowledge distillation and paper production computing system processes instructions to use machine learning models to automatically review papers from a large corpus of papers and distill knowledge using science of science methods and AI-based modeling techniques. The AI-based knowledge distillation and paper production computing system processes instructions to leverage network science and machine learning tools to analyze papers with respect to a given topic to find relevant scientific publications, organize and group publications based on topic similarity and relation to the topic in general, and distill and summarize the message and content of these publications into a coherent set of statements.

Abstract: A system to predict success includes a memory configured to store failure data. The failure data includes information regarding one or more failed attempts to achieve a goal. The system also includes a processor operatively coupled to the memory. The processor is configured to analyze the failure data. The processor is also configured to determine, with an algorithm, a likelihood of success that the goal will be achieved on a subsequent attempt. The likelihood of success is based at least in part on the analysis of the failure data.

Abstract: A method includes receiving a set of parameters for a given project and generating, using information from a knowledge database, a plurality of combinations of group members based at least in part on the set of parameters. The method also includes evaluating a set of metrics for each of the combinations of group members, the set of metrics comprising at least one novelty metric and at least one collective intelligence metric. The method further includes generating one or more strategy matrices for each of at least a subset of the combinations of group members using information from the knowledge database, evaluating the combinations of group members in the subset using the strategy matrices to determine respective predicted success values, and selecting a given one of the combinations of group members for the given project based at least in part on the sets of metrics and predicted success values.

Abstract: A method includes receiving a set of parameters for a given project and generating, using information from a knowledge database, a plurality of combinations of group members based at least in part on the set of parameters. The method also includes evaluating a set of metrics for each of the combinations of group members, the set of metrics comprising at least one novelty metric and at least one collective intelligence metric. The method further includes generating one or more strategy matrices for each of at least a subset of the combinations of group members using information from the knowledge database, evaluating the combinations of group members in the subset using the strategy matrices to determine respective predicted success values, and selecting a given one of the combinations of group members for the given project based at least in part on the sets of metrics and predicted success values.

Abstract: A method includes receiving a set of parameters for a given project and generating, using information from a knowledge database, a plurality of combinations of group members based at least in part on the set of parameters. The method also includes evaluating a set of metrics for each of the combinations of group members, the set of metrics comprising at least one novelty metric and at least one collective intelligence metric. The method further includes generating one or more strategy matrices for each of at least a subset of the combinations of group members using information from the knowledge database, evaluating the combinations of group members in the subset using the strategy matrices to determine respective predicted success values, and selecting a given one of the combinations of group members for the given project based at least in part on the sets of metrics and predicted success values.

Abstract: A method includes receiving a set of parameters for a given project and generating, using information from a knowledge database, a plurality of combinations of group members based at least in part on the set of parameters. The method also includes evaluating a set of metrics for each of the combinations of group members, the set of metrics comprising at least one novelty metric and at least one collective intelligence metric. The method further includes generating one or more strategy matrices for each of at least a subset of the combinations of group members using information from the knowledge database, evaluating the combinations of group members in the subset using the strategy matrices to determine respective predicted success values, and selecting a given one of the combinations of group members for the given project based at least in part on the sets of metrics and predicted success values.

Abstract: A computing device identifies one or more contextual variables within one or more social media messages. The computing device determines a contextual influence value based on the one or more social media messages. The computing device determines an appetite level. The computing device determines an unadjusted expected value of pleasantness based on the determined contextual influence value and the determined appetite level.

Abstract: Various embodiments quantify herding effects in one or more collective rating systems. In one embodiment, a set of historical rating data associated with at least one rated entity and generated by a collective rating system is obtained. The set of historical rating data at least includes a sequence of ratings and a distribution of ratings in the sequence of ratings at each of a set of rating-levels. An optimal setting for each of a set of parameters and at least one function associated with a prediction-based model is calculated utilizing the set of historical rating data, where each of the optimal settings satisfies an optimization threshold. The prediction-based model is configured with the optimal setting for each of the set of parameters and at least one function. A set of modeling data is generated based on the configured prediction-based model and the set of historical rating data.