{"id":995562,"date":"2024-01-05T08:06:06","date_gmt":"2024-01-05T16:06:06","guid":{"rendered":"https:\/\/www.microsoft.com\/en-us\/research\/?post_type=msr-project&p=995562"},"modified":"2024-10-22T12:56:03","modified_gmt":"2024-10-22T19:56:03","slug":"afmr-creativity-and-design","status":"publish","type":"msr-project","link":"https:\/\/www.microsoft.com\/en-us\/research\/project\/afmr-creativity-and-design\/","title":{"rendered":"AFMR: Creativity and Design"},"content":{"rendered":"
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Creativity and Design<\/h1>\n\n\n\n

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Academic research plays such an important role in advancing science, technology, culture, and society. This grant program helps ensure this community has access to the latest and leading AI models.<\/em><\/strong><\/p>\nBrad Smith, Vice Chair and President<\/cite><\/blockquote>\n\n\n\n

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AFMR Goal: Improve human interactions via sociotechnical research<\/h2>\n\n\n\n

which increases trust, human ingenuity, creativity, and productivity, and decreases the digital divide while reducing the risks of developing AI which does not benefit individuals and society<\/p>\n<\/div>\n\n\n\n

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These projects examine how AI can be used across different disciplines to support human creativity, improve workflow efficiency, and enrich the user experience. Some of the notable projects include using these models to inspire human creativity, and applying AI-transformed applications for immersive learning, coaching, and language learning. Moreover, proposals aim to improve the efficiency of AI-powered agents with advanced language communication tools and explore how AI can enhance creativity in virtual reality settings. Another interesting venture is to look into how AI can make academic insights more accessible for practitioners, and how it can comprehend and utilize emotional intelligence. Methodologies range from host studio sessions, design workshops, experiments and computational frameworks, among others. We expect these initiatives to lead to innovations that can address the current challenges, provide tools that can improve user experience and facilitate learning and creativity, and new discoveries in the understanding of AI’s emotional intelligence ability.<\/p>\n\n\n\n

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Prairie View A&M University <\/strong>: Malachi Crawford (PI)<\/p>\n\n\n\n

Briefly, this research project seeks to evaluate the use of large language models, such as GPT-4 and DALL-E 2, in the creation of a stage play from primary source data. Through a process of AI prompting and iteration, we will develop a rubric to evaluate the model’s performance across five indices: character development, character dialogue, plot structure, set design\/visual elements, and stage direction. Ultimately, we anticipate AI diminishing the barriers, such as time spent writing dialogue, scene creation, outlining stage direction, and other highly-skilled artistic activity that might constrain students of history from bridging the historical profession with the creative and performative arts. In so doing, this proposal aligns with the “Advance Beneficial Applications of AI” research component.<\/p>\n\n\n\n\n\n

Cornell University<\/strong>: Jeffrey Rzeszotarski (PI)<\/p>\n\n\n\n

This project outlines two experiments aimed at understanding how generative models can support human creativity. It explores the integration of DALL-E 2 into a creative drawing lab experiment and how GPT-4 can be integrated into an existing qualitative thematic coding tool to enhance user experience. By focusing on these successful AI models, it seeks to mitigate the potential risks of their application in creative tasks.<\/p>\n\n\n\n\n\n

The University of Texas at Arlington<\/strong>: Cesar Torres (PI)<\/p>\n\n\n\n

Bricolage is a creative practice emphasizing the influence of a practitioner’s environment (physical, social, or otherwise) on their creative process. In contrast to an empty room or screen, bricolage posits that spaces filled with possibilities (e.g., via galleries of artifacts, varieties of tools, diverse peoples) encourage improvisation, innovation, and resourcefulness; however, such spaces often evolve over decades of practice. Large language models offer a unique opportunity to generate conceptual resources to enhance bricolage practice across a variety of disciplines. Leveraging our current work on harvesting video tutorial transcripts from 30 communities of practice, this work aims to tune foundational models into practice-tuned LLMs (e.g., ElectronicsGPT, 3DPrintingGPT, CeramicsGPT, PromptGPT). These models will be used to extract and organize information about techniques, tools, and materials into accessible formats, such as dynamic AI-generated image collages and flow charts. This approach will offer a collection of bricolage resources to enhance human creativity, promote interdisciplinary innovation, and serve as a bridge for more practitioners to leverage AI within their respective practices.<\/p>\n\n\n\n\n\n

IIT Tirupati<\/strong>: Sridhar Chimalakonda (PI)<\/p>\n\n\n\n

Moving away from the predominantly common approach of hand-crafting source code representations [e.g. Abstract Syntax Tree (AST), Control Flow Graphs (CFG)] and AI pipelines for a given software engineering task (e.g. Code Summarization, Bug Localization, Code Clone Detection), the proposed project aims to find the appropriate mix of spruce code representation for a given SE task. Specifically, the proposed project builds on our mocktail approach and aims to create a a framework that can facilitate configuring and experimenting with different types and combinations of source code representations and ML models for various SE tasks. We see that this framework can help researchers and practitioners to explore, experiment and build an appropriate AI pipeline for a given SE task without manually creating each instance of the AI pipeline.<\/p>\n\n\n\n\n\n

Prairie View A&M University<\/strong>: Malachi Crawford (PI)<\/p>\n\n\n\n

Briefly, this research project seeks to evaluate the use of large language models, such as GPT-4 and DALL-E 2, in the creation of a stage play from primary source data. Through a process of AI prompting and iteration, we will develop a rubric to evaluate the model’s performance across five indices: character development, character dialogue, plot structure, set design\/visual elements, and stage direction. Ultimately, we anticipate AI diminishing the barriers, such as time spent writing dialogue, scene creation, outlining stage direction, and other highly-skilled artistic activity that might constrain students of history from bridging the historical profession with the creative and performative arts. In so doing, this proposal aligns with the Advance Beneficial Applications of AI research component.<\/p>\n\n\n\n\n\n

William & Mary<\/strong>: Yixuan Zhang (PI)<\/p>\n\n\n\n

This proposal aims to investigate the capacity of LLMs to understand and harness emotional intelligence. The research expands emotional stimuli used in LLMs, assesses the depth and duration of emotional interaction with LLMs, and its influence on people\u2019s perceptions and trust.<\/p>\n\n\n\n

Related papers:<\/strong><\/p>\n\n\n\n