Summary of Problem Solving Through Human-ai Preference-based Cooperation, by Subhabrata Dutta et al.
Problem Solving Through Human-AI Preference-Based Cooperation
by Subhabrata Dutta, Timo Kaufmann, Goran Glavaš, Ivan Habernal, Kristian Kersting, Frauke Kreuter, Mira Mezini, Iryna Gurevych, Eyke Hüllermeier, Hinrich Schuetze
First submitted to arxiv on: 14 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Human-Computer Interaction (cs.HC)
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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 The proposed HAI-Co2 framework aims to address the limitations of current generative AI by enabling human-AI cooperation in complex problem-solving tasks. The framework is designed to overcome shortcomings such as tracking complex solution artifacts, supporting versatile human preference expression, and adapting to human preferences in interactive settings. By formalizing HAI-Co2 and identifying open research problems, the authors demonstrate its efficacy compared to monolithic generative AI models in a case study. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is getting really smart, but it’s not as good at solving complex problems as humans are. This paper talks about how AI can work better with people to solve these problems. The current state of AI isn’t very helpful because it can’t keep track of complicated solutions or understand what we want it to do. To fix this, the authors suggest a new way for humans and AI to work together called HAI-Co2. They explain how this works and show that it’s better than just using AI alone. |
Keywords
» Artificial intelligence » Tracking
