Artificial Intelligence/Machine Learning, ASA(ALT), Direct to Phase II
Natural Language Processing
Objective
The objective of this topic is to develop Artificial Intelligence / Machine Learning models to augment Natural Language Processing (NLP) capabilities in 2 main challenge areas: relationship detection and aggregation – automatically detecting relationships that exist between the entities that were extracted from the data. Some extracting attributes of entities include: hair color, nationality, model of tank, armor of tank. Pattern recognition, analysis, and exploitation – automatically recognizing patterns such as indications and warnings and courses of actions and analyzing them – is an integral part of this topic.
Description
The purpose of this topic is to demonstrate how novel approaches and techniques can address these challenge areas and to develop prototypes that can be transitioned into PM IS&A’s products. PM IS&A products will empower the intel analyst by providing them with access to critical data/information and advanced analytics. NLP is a critical capability for PM IS&A products that is necessary to make sense of large amounts of unstructured data from multiple unclassified and classified sources. Unfortunately, today’s technology struggles at adequately addressing this problem. This should be done by reducing the cognitive load on the intel analyst, improving situational awareness and situational understanding for the war fighter and intel analyst, addressing gaps in the existing NLP technologies, and improving data fusion and pattern recognition and analysis tools that are used for intelligence applications. Currently, information is manually extracted by people. Today, intel analysts spend half of their time looking for the golden nugget in massive amounts of data sets by using a mixture of techniques and tools. Today’s NLP technologies excel at very specific tasks such as identifying attributes and extracting attributes from entities (not correlating back to either other). We propose developing AI/ML models to help augment the NLP technologies. Based on Industry Engagements over the last 12 months, small portion of Industry Partners have started to introduce AI/ML modeling to improve NLP performance. This provides us confidence in the approach to leverage AI/ML modeling to augment NLP technology
Phase I
In order for a work to proceed directly to the Phase II level (a Direct to Phase II or DP2), proposing firms must be able to substantiate that their technology is currently at an acceptable stage and that the scientific and technical merit and feasibility at the Phase I level has already been met. The proposal package should clearly describe the scientific, technical, and commercial merits of the proposed concept and the resulting prototype that may have been developed. Documents that should be provided for each of the areas are indicated below. As part of your proposal package, please provide any of these documents that presently exist, or other documentation that exists which you feel provides comparable or additionally-relevant information. Scientific Merit may include: list of peer reviewed whitepapers and/or conference proceedings that have been published or presented on the concept; any awards, writeups, endorsements, or recognition received. Technical Merit may include description of the technical problem that the specific problem will attempt to solve and how; technical demo (recorded); technical report that describes key technical performance parameters such as results, description of technical approach with diagrams, and/or description of technical rigor that is employed. Commercial Merit may include: strategy for commercializing the technology and transitions it to government/industry; commercialization roadmap including costs and schedule; perceived impact or value and how technology can be integrated in a commercial setting. Feasibility Studies that address these areas are: legal, economic, operational, technical, and scheduling.
Phase II
Phase II will consist of awarding a few proposals to be expanded upon through additional Research and Technology Development, which will lead to Early Prototyping.
Phase III
Phase III will consist of the transition to Commercialization phase to PM IS&A
For more information, and to submit your full proposal package, visit the DSIP Portal.
Objective
The objective of this topic is to develop Artificial Intelligence / Machine Learning models to augment Natural Language Processing (NLP) capabilities in 2 main challenge areas: relationship detection and aggregation – automatically detecting relationships that exist between the entities that were extracted from the data. Some extracting attributes of entities include: hair color, nationality, model of tank, armor of tank. Pattern recognition, analysis, and exploitation – automatically recognizing patterns such as indications and warnings and courses of actions and analyzing them – is an integral part of this topic.
Description
The purpose of this topic is to demonstrate how novel approaches and techniques can address these challenge areas and to develop prototypes that can be transitioned into PM IS&A’s products. PM IS&A products will empower the intel analyst by providing them with access to critical data/information and advanced analytics. NLP is a critical capability for PM IS&A products that is necessary to make sense of large amounts of unstructured data from multiple unclassified and classified sources. Unfortunately, today’s technology struggles at adequately addressing this problem. This should be done by reducing the cognitive load on the intel analyst, improving situational awareness and situational understanding for the war fighter and intel analyst, addressing gaps in the existing NLP technologies, and improving data fusion and pattern recognition and analysis tools that are used for intelligence applications. Currently, information is manually extracted by people. Today, intel analysts spend half of their time looking for the golden nugget in massive amounts of data sets by using a mixture of techniques and tools. Today’s NLP technologies excel at very specific tasks such as identifying attributes and extracting attributes from entities (not correlating back to either other). We propose developing AI/ML models to help augment the NLP technologies. Based on Industry Engagements over the last 12 months, small portion of Industry Partners have started to introduce AI/ML modeling to improve NLP performance. This provides us confidence in the approach to leverage AI/ML modeling to augment NLP technology
Phase I
In order for a work to proceed directly to the Phase II level (a Direct to Phase II or DP2), proposing firms must be able to substantiate that their technology is currently at an acceptable stage and that the scientific and technical merit and feasibility at the Phase I level has already been met. The proposal package should clearly describe the scientific, technical, and commercial merits of the proposed concept and the resulting prototype that may have been developed. Documents that should be provided for each of the areas are indicated below. As part of your proposal package, please provide any of these documents that presently exist, or other documentation that exists which you feel provides comparable or additionally-relevant information. Scientific Merit may include: list of peer reviewed whitepapers and/or conference proceedings that have been published or presented on the concept; any awards, writeups, endorsements, or recognition received. Technical Merit may include description of the technical problem that the specific problem will attempt to solve and how; technical demo (recorded); technical report that describes key technical performance parameters such as results, description of technical approach with diagrams, and/or description of technical rigor that is employed. Commercial Merit may include: strategy for commercializing the technology and transitions it to government/industry; commercialization roadmap including costs and schedule; perceived impact or value and how technology can be integrated in a commercial setting. Feasibility Studies that address these areas are: legal, economic, operational, technical, and scheduling.
Phase II
Phase II will consist of awarding a few proposals to be expanded upon through additional Research and Technology Development, which will lead to Early Prototyping.
Phase III
Phase III will consist of the transition to Commercialization phase to PM IS&A
For more information, and to submit your full proposal package, visit the DSIP Portal.