Summary of Evaluating Frontier Models For Dangerous Capabilities, by Mary Phuong et al.
Evaluating Frontier Models for Dangerous Capabilities
by Mary Phuong, Matthew Aitchison, Elliot Catt, Sarah Cogan, Alexandre Kaskasoli, Victoria Krakovna, David Lindner, Matthew Rahtz, Yannis Assael, Sarah Hodkinson, Heidi Howard, Tom Lieberum, Ramana Kumar, Maria Abi Raad, Albert Webson, Lewis Ho, Sharon Lin, Sebastian Farquhar, Marcus Hutter, Gregoire Deletang, Anian Ruoss, Seliem El-Sayed, Sasha Brown, Anca Dragan, Rohin Shah, Allan Dafoe, Toby Shevlane
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 In this research paper, a new approach to evaluating the risks posed by artificial intelligence (AI) systems is introduced. The authors build upon prior work and pilot their “dangerous capability” evaluations on Gemini 1.0 models, covering four areas: persuasion and deception, cyber-security, self-proliferation, and self-reasoning. While no strong dangerous capabilities were found in the evaluated models, early warning signs are flagged. The goal is to advance a rigorous science of dangerous capability evaluation, preparing for future AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers want to know what new AI can do and can’t do. To help with this, they’re testing how well these AIs can be tricked or convince others. They also looked at cybersecurity risks and the AIs’ ability to create more of themselves or think on their own. The good news is that the AIs tested didn’t show strong dangerous capabilities, but some warning signs were noticed. This study aims to make AI safer by understanding its limits. |
Keywords
* Artificial intelligence * Gemini
