Summary of Research on Color Recipe Recommendation Based on Unstructured Data Using Tenn, by Seongsu Jhang et al.
Research on color recipe recommendation based on unstructured data using TENN
by Seongsu Jhang, Donghwi Yoo, Jaeyong Kown
First submitted to arxiv on: 17 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel approach is proposed in this paper to infer color recipes using large language models and unstructured data, which can benefit industries that rely heavily on human emotions and senses. The increasing sophistication of algorithms that can understand and apply human language has led to the development of services like OpenAI Chatgpt, Google BARD, and Microsoft copilot. However, applying these algorithms to processes that require human intuition and creativity is challenging. The paper focuses on the color mixing process in industries such as painting and injection molding, where small and medium-sized companies rely on tacit knowledge and sensibility of color mixers. To address this challenge, the authors propose TENN, a model that infers color recipes based on unstructured data with emotional natural language. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to use big language models to help industries that need human emotions and senses. These models are getting better at understanding and using human language, but it’s hard to apply them to tasks that require creativity and intuition. The authors focus on the color mixing process in industries like painting and injection molding, where small companies rely on the experience of their color mixers. They propose a new model called TENN that can infer color recipes based on unstructured data with emotional natural language. |
