Proposals for 2023#
This section contains a list of proposals for 2023. Each proposal is a link to a page with more information about the proposal. Contents are subject to change and there is no guarantee that any of these proposals will be completed.
Creative ESG Alliance: This proposal aims to create a global alliance of artists, scientists, and ESG (Environmental, Social, and Governance) professionals to promote sustainable development through creative projects. The alliance will use AI and data science to analyze ESG data and create impactful art that raises awareness about sustainability issues.
Meta-Human Professor Platform: This proposal is for the development of a platform that uses AI to create virtual professors. These “meta-human professors” will be able to provide personalized education to students, using AI to adapt their teaching methods to each student’s learning style and pace. The platform will also include a system for peer review and quality control.
Embeddings for Economic Indicators and Text Data: This proposal aims to develop a new method for analyzing economic data using AI. The method involves creating “embeddings” for economic indicators and text data, which can then be used to predict economic trends and make policy recommendations.
Central Bank Communication Strategy and Tools: This proposal aims to develop new communication strategies and tools for the Central Bank of Cambodia. The project will use text mining and machine learning techniques to analyze the effects of the bank’s communication strategies and propose improvements.
Predicting Corporate Distress: This proposal is for a new method for predicting corporate credit risk. The method combines traditional models with text mining techniques to analyze both structured and unstructured data. The goal is to provide a more accurate and holistic view of a company’s financial health.
Bibformer: This proposal introduces Bibformer, a novel approach to bibliometric and scientometric analysis that leverages the power of Large Language Models (LLMs) to automate interpretation, perform semantic analysis, and predict trends. By incorporating vector embeddings of scientific papers into graph network methodologies, Bibformer provides a richer representation of bibliometric data and enables more nuanced analyses.
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