少点错误 04月07日 19:37
RFC: a tool to create a ranked list of projects in explainable AI
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本文介绍了一个从ICLR 2025会议论文中提取项目的工具,并采用类似锦标赛的方式对项目进行影响力排序。该工具旨在降低AI研究的入门门槛,鼓励更多人参与其中,特别是针对本科生和对AI对齐感兴趣的研究者。作者希望通过构建实用工具来促进学习,并征求用户反馈以改进该工具,包括项目提取、排序、用户界面和推广等方面。该工具目前处于早期阶段,欢迎大家提出建议,共同完善。

💡该工具的核心功能是从ICLR 2025会议论文中提取项目,并基于影响力进行排序,类似于锦标赛的排名方式。

👨‍🎓该工具特别关注于降低AI研究的入门门槛,旨在吸引更多人参与,特别是本科生和对AI对齐感兴趣的研究者。

🔍项目排名目前是主观和自动的,未来可能会加入基于研究人员直觉的加权投票系统来进行重新排序。

🛠️作者希望通过构建实用工具来促进学习,并征求用户反馈,以改进项目提取、排序、用户界面和推广等方面。

Published on April 6, 2025 9:18 PM GMT

TL; DR

Inspired by a recent post by Neel Nanda on Research Directions, I'm building a tool that extracts projects from ICLR 2025 and uses tournament-like ranking of them based on how impactful they are. You can see them here https://openreview-copilot.eamag.me/projects if you filter by primary area. There are many ways to improve it, starting from but I want to get your feedback on how useful it is and what are the most important things to iterate on.

Why

I think the best way to learn things is by building something. People in universities are building simple apps to learn how to code, for example. Won't it be better if they were building something that's more useful for the world? I'm extracting projects from recent ML papers based on different level of competency, from no-coding to PhD. I rank undergraduate-level projects (mostly in explainable AI area, but also just top ranked papers from that conference) to find the most useful. More details on the motivation and implementation are in the linked post.

We can probably increase the speed of research in AI alignment by involving more people in it, and to do so we have to lower the barriers of entry, and prove that the things people can work on are actually meaningful. The ranking now is subjective and automatic, but it's possible to add another (weighed) voting system on top to rerank projects based on researchers' intuition.

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