This repository archives discussions and summaries of weekly AI research papers from arXiv, targeting AI researchers and enthusiasts. It facilitates a lightweight, high-level overview of novel and interesting papers, encouraging deeper analysis by participants, and is presented through Zoom webinars and Facebook Live streams.

How It Works

The project operates as a community-driven knowledge-sharing platform. Weekly issues are created on GitHub, where participants can suggest papers via replies, including a link and a brief summary or screenshot. Selected papers are then discussed in a casual, "talk-show" style format during live sessions, prioritizing breadth and novelty over in-depth technical analysis.

Quick Start & Requirements

• Participation: No installation required. Join live sessions via provided Zoom webinar links or watch recordings on Facebook.
• Contribution: Submit paper suggestions as GitHub issue replies.
• Resources: Access to Zoom and Facebook.

Highlighted Details

• Features weekly discussions of AI arXiv papers, covering a wide range of topics.
• Content is presented in a casual, "talk-show" format, emphasizing personal interest and novelty.
• Provides links to past recordings on Facebook and Zoom webinar details for live participation.
• Encourages community contribution and discussion on paper significance.

Maintenance & Community

The project is maintained by a core group of moderators, with a list of alumni and special thanks indicating community involvement. Discussions and past episodes are archived, with new sessions announced.

Licensing & Compatibility

The repository's content and activities appear to be for personal study and relation, with a disclaimer stating no affiliation with the author's employer. No specific open-source license is mentioned for the repository's content itself.

Limitations & Caveats

The project focuses on a "surface-level" overview of papers, explicitly stating that in-depth analysis is left to participants. The content is curated based on personal preference, meaning coverage may not be exhaustive or systematically representative of the entire AI research landscape.