Abstract: In some aspects, systems and methods for rapidly building, managing, and sharing machine learning models are provided. Managing the lifecycle of machine learning models can include: receiving a set of unannotated data; requesting annotations of samples of the unannotated data to produce an annotated set of data; building a machine learning model based on the annotated set of data; deploying the machine learning model to a client system, wherein production annotations are generated; collecting the generated production annotations and generating a new machine learning model incorporating the production annotations; and selecting one of the machine learning model built based on the annotated set of data or the new machine learning model.

Abstract: In some aspects, systems and methods for rapidly building, managing, and sharing machine learning models are provided. Managing the lifecycle of machine learning models can include: receiving a set of unannotated data; requesting annotations of samples of the unannotated data to produce an annotated set of data; building a machine learning model based on the annotated set of data; deploying the machine learning model to a client system, wherein production annotations are generated; collecting the generated production annotations and generating a new machine learning model incorporating the production annotations; and selecting one of the machine learning model built based on the annotated set of data or the new machine learning model.

Abstract: A system and method for processing information in unstructured or structured form, comprising a computer running in a distributed network with one or more data agents. Associations of natural language artifacts may be learned from natural language artifacts in unstructured data sources, and semantic and syntactic relationships may be learned in structured data sources, using grouping based on a criteria of shared features that are dynamically determined without the use of a priori classifications, by employing conditional probability constraints.

Abstract: A system and method for processing information in unstructured or structured form, comprising a computer running in a distributed network with one or more data agents. Associations of natural language artifacts may be learned from natural language artifacts in unstructured data sources, and semantic and syntactic relationships may be learned in structured data sources, using grouping based on a criteria of shared features that are dynamically determined without the use of a priori classifications, by employing conditional probability constraints.

Abstract: Disclosed is a batten riser assembly that is used to install battens for tile roofs. The batten riser assembly can be used to prolong the life of tile roofs since it is made of waterproof materials. The batten riser assembly uses flexible strips that can be easily folded in a compact package and can be installed in a quick and easy manner. The batten riser assembly can be used in conjunction with a marker tape to speed installation. The batten riser assembly is designed to prevent the damming of water that penetrates the tile roof. The batten riser assembly provides a simple and easy way for installers to comply with building codes.

Abstract: A software agent system is provided that continues to learn as it utilizes natural language processors to tackle limited semantic awareness, and creates superior communication between disparate computer systems. The software provides intelligent middleware and advanced learning agents which extend the parameters for machine agent capabilities beyond simple, fixed tasks thus producing cost savings in future hardware and software platforms.