Summary of Large Scale Generative Ai Text Applied to Sports and Music, by Aaron Baughman et al.
Large Scale Generative AI Text Applied to Sports and Music
by Aaron Baughman, Stephen Hammer, Rahul Agarwal, Gozde Akay, Eduardo Morales, Tony Johnson, Leonid Karlinsky, Rogerio Feris
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 paper presents a solution to scale up media content production for large-scale sports and music events. The approach utilizes generative AI models to transform multimodal data into coherent text. A novel AI commentary system was developed, deployed at major tennis tournaments, and used to create personalized content for ESPN Fantasy Football and Grammy stories. This architecture achieved a 15x speed improvement with high Rouge-L and perplexity scores. The successful deployment supported over 90 million fans worldwide, pushing the boundaries of sports, entertainment, and AI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes it possible to produce lots of media content quickly, like news articles or commentary for big sports events. They use special AI models that can understand many types of data, like videos, articles, and scores. This helps create personalized stories for fans. The system was tested at some famous tennis tournaments and even helped with ESPN Fantasy Football. It worked really well, making a lot of content in no time. |
Keywords
» Artificial intelligence » Perplexity » Rouge
