Unlocking Visual Creativity: Harnessing Large Natural Language Models to Convert Human Language into Graphical Representations

The ability to convert human language, such as film scripts, novels, or podcast transcripts, into structured data that can be transformed into graphical assets opens new avenues for visual creativity.

This abstract explores the utilization of large natural language models to unlock this potential, enabling the transformation of textual content into captivating graphical representations.

By leveraging the power of these models, the process of converting human language into graphical assets becomes more efficient, accurate, and scalable. This abstract delves into the underlying techniques and methodologies employed in this conversion process, highlighting the benefits and challenges associated with the integration of natural language processing and graphic design.

Furthermore, it discusses the implications of this transformative approach in various fields, including entertainment, advertising, education, and beyond. Ultimately, by harnessing large natural language models, we can unlock visual creativity, enhancing the way we communicate and visualize textual information in a visually compelling manner.


About the speaker

Fatema Nafa

Professor at Salem State University

I am a data scientist researcher who examines how to design and exploit algorithms to study time series datasets. I am committed to using my love and talent for teaching and research to empower others through education to make a difference in the world. Inspiring and motivating students by providing a thorough understanding of a variety of computer and data science technology concepts, subjects, and current research issues.

I offer a demonstrated history of successfully implementing technology-driven learning tools and assessment methods. My current research interests are broad and varied, but generally fall into Machine Learning, Data Visualization, and large natural language models.




Online Event: October 3-5, 2023




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