Abstract: A storage maintains raw image data including video having a sequence of frames, and annotations of the frames that indicate aspects of objects identified in the respective frames. A processor determines, for annotation of key frames of the raw image data, a task type for key frames and an agent type for key frames, receives annotations of objects identified in the key frames of the raw image data according to the key frame task type and key frame agent type, selects to review the key frames based on a confidence level of the annotations of the key frames, determines, for annotation of intermediate frames of the raw image data, a task type for intermediate frames and an agent type for intermediate frames, and receives annotations of objects identified in the intermediate frames of the raw image data according to the intermediate frame task type and intermediate frame agent type.

Abstract: A data annotation system for a machine-learning system includes a computing system configured to execute a machine-learning algorithm for generating data annotations of input data. The computing system is further configured to execute an interface for communicating results and predictions of the machine-learning algorithm to non-expert workers for review. The interface communicates with the non-expert worker using visualization and natural language to elicit feedback to improve performance of the machine-learning algorithm.

Abstract: A decision-support system for a public transportation system includes a computing system programmed to generate public transit route options for the commuter based on at least one decision factor that estimates an expected impact on other commuters resulting from the commuter choosing each of the public transit route options, and output, for display, values associated with the at least one decision factor to influence the commuter in making a route selection.

Abstract: A crowdsourced dialogue system includes a first and second computer system, a training system, and an automated response system. The first computer system includes a caller interface operated by a non-expert user and to generate caller dialogue data based on inputs to the first computer system from the non-expert user. The second computer system includes an expert interface operated by an expert user and to generate expert dialogue data based on inputs to the second computer system from the expert user. The training system includes a general domain dialogue database. The training system is configured to generate a domain-specific dialogue database based on the caller dialogue data and the expert dialogue data. The automated response system includes a chatbot that is trained with the general domain dialogue database and the domain-specific dialogue database to generate natural language dialogue data at least in a domain of the domain-specific dialogue database.

Abstract: A storage maintains raw image data including video having a sequence of frames, and annotations of the frames that indicate aspects of objects identified in the respective frames. A processor determines, for annotation of key frames of the raw image data, a task type for key frames and an agent type for key frames, receives annotations of objects identified in the key frames of the raw image data according to the key frame task type and key frame agent type, selects to review the key frames based on a confidence level of the annotations of the key frames, determines, for annotation of intermediate frames of the raw image data, a task type for intermediate frames and an agent type for intermediate frames, and receives annotations of objects identified in the intermediate frames of the raw image data according to the intermediate frame task type and intermediate frame agent type.

Abstract: A data annotation system for a machine-learning system includes a computing system configured to execute a machine-learning algorithm for generating data annotations of input data. The computing system is further configured to execute an interface for communicating results and predictions of the machine-learning algorithm to non-expert workers for review. The interface communicates with the non-expert worker using visualization and natural language to elicit feedback to improve performance of the machine-learning algorithm.

Abstract: A system and method is disclosed for adjusting control of an autonomous vehicle based on crowd-source data. The autonomous vehicle may be designed to receive crowd-source data relating to a driving condition located along a travel route the autonomous vehicle is travelling. The control of the autonomous vehicle may then be adjusted in response to the crowd-source data provided. The autonomous vehicle may also request crowd-source data related to how the autonomous vehicle should proceed along a travel route. Based on the request, the autonomous vehicle may receive crowd-source data instructing the autonomous vehicle how to proceed along the travel route. The autonomous vehicle may also adjust how the autonomous vehicle proceeds along the travel route in response to the crowd-source data.

Abstract: A decision-support system for a public transportation system includes a computing system programmed to generate public transit route options for the commuter based on at least one decision factor that estimates an expected impact on other commuters resulting from the commuter choosing each of the public transit route options, and output, for display, values associated with the at least one decision factor to influence the commuter in making a route selection.

Abstract: An information synthesis system for generating a knowledge base injects domain knowledge into semi-crowdsourced summarization pipelines for extracting information from unstructured data sources. The summarization pipeline includes chains of tasks completed by crowd workers and/or machined. The information synthesis system distributes the tasks to crowd workers and/or machines. Task responses are processed and aggregated to determine new information that is used to update the knowledge base.

Abstract: A crowdsourced dialogue system includes a first and second computer system, a training system, and an automated response system. The first computer system includes a caller interface operated by a non-expert user and to generate caller dialogue data based on inputs to the first computer system from the non-expert user. The second computer system includes an expert interface operated by an expert user and to generate expert dialogue data based on inputs to the second computer system from the expert user. The training system includes a general domain dialogue database. The training system is configured to generate a domain-specific dialogue database based on the caller dialogue data and the expert dialogue data. The automated response system includes a chatbot that is trained with the general domain dialogue database and the domain-specific dialogue database to generate natural language dialogue data at least in a domain of the domain-specific dialogue database.