# Full Text: Enhanced NSF Postdoctoral Reporting via Synthetic Intelligence Language Processing

> Extracted from `2023_NSFReporting.pdf`

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## Page 1

Enhanced NSF Postdoctoral Reporting via
Synthetic Intelligence Language Processing
Daniel Ari Friedman ~ Daniel@ActiveInference.Institute
11/20/2023 ~ v1 ~ 10.5281/zenodo.10160657
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Objective:
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Refine postdoctoral reporting at the National Science Foundation (NSF) through generative
intelligence systems, bolstering efficiency and broadening dissemination scope.
Core Components:
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Updatable Profiles
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Postdocs provide structured and unstructured data regarding deliverables, research
progress, insights, collaborations, and more.
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Social media and websites can be included for a comprehensive digital footprint.
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Intelligent Processing Prompts
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Employ prompt engineering and synthetic intelligence approaches to coherently reformat
and standardize submissions, like converting publication details into bullet points without
distorting factual content.
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Dynamic Reporting System
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Generates real-time, evolving reports, substantially reducing administrative demands for
postdocs and NSF program managers.
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Reports are adaptable in multiple linguistic outputs (length, language, genre) and diverse
media forms, including imagery, video, and augmented/virtual reality environments.
Strategic and Tactical Advantages:
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Continuous Evaluation: Enables real-time monitoring and appraisal of postdoctoral activities,
potentially leading to proactive contacts, interventions, or tailored advice during the fellowship.
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Operational Efficiency: Significantly alleviates postdocs’ administrative responsibilities and
uncertainties, thus channeling more energy into research endeavors.
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Consistent Reporting Framework: Assures uniformity in reporting, streamlining data analysis
and comparative studies.
Implementation Strategy:
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Constitution by Composition: Develop a coherent and functional system architecture enabling
the proposed features.
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User-Centric Interface Design: Craft an intuitive, user-friendly platform integrating advanced
language processing capabilities.
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Robust Cyber and Cognitive Security: Prioritize the integrity of all submitted information.
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Adaptive System Evolution: Organizational and Ecosystem-scale commitment to an ongoing
process of refinement and enhancement based on user feedback and technological progress.


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*Extraction method: pymupdf*