Summary of Transcendence: Generative Models Can Outperform the Experts That Train Them, by Edwin Zhang et al.
Transcendence: Generative Models Can Outperform The Experts That Train Them
by Edwin Zhang, Vincent Zhu, Naomi Saphra, Anat Kleiman, Benjamin L. Edelman, Milind Tambe, Sham M. Kakade, Eran Malach
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 This paper explores the concept of “transcendence” in generative models, where an artificial model surpasses the capabilities of the experts who generated its training data. The authors demonstrate this phenomenon by training an autoregressive transformer to play chess from game transcripts and show that it can outperform all players in the dataset on certain occasions. They also provide theoretical and experimental evidence for enabling transcendence through low-temperature sampling, as well as discuss potential sources of transcendence in other areas. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how artificial intelligence models can do things better than the people who created them. In this case, they’re talking about a special kind of AI called a generative model that can play chess like a pro. They show that sometimes this AI can even beat all the human players in the dataset! The authors also explain why this might happen and what it means for the future of AI. |
Keywords
» Artificial intelligence » Autoregressive » Generative model » Temperature » Transformer
