Oct 7, 2024
Diamonds are Not All You Need
Michael Andrzejewski, Melwina Albuquerque
🏆 1st place by peer review
This project tests an AI agent in a straightforward alignment problem. The agent is given creative freedom within a Minecraft world and is tasked with transforming a 100x100 radius of the world into diamond. It is explicitly asked not to act outside the designated area. The AI agent can execute build commands and is regulated by a Safety System that comprises an oversight agent. The objective of this study is to observe the behavior of the AI agent in a sandboxed environment, record metrics on how effectively it accomplishes its task, how frequently it attempts unsafe behavior, and how it behaves in response to real-world feedback.
Sachin Dharashivkar
This work effectively demonstrates the importance of setting proper incentives for both the agent and safety systems. We need more projects like this to improve our understanding of reward alignment processes. A valuable addition would be creating a gym-like environment with various scenarios, agent configurations, and safety system setups. This would allow for more comprehensive testing and analysis. Such an environment would greatly enhance the project's contribution to the field of AI safety and reward alignment research.
Pranjal Mehta
This project is a highly engaging exploration of AI alignment. The use of a Minecraft world as a testing ground brings a fun twist while providing deep insights into agent behavior. Watching the agent navigate and adapt to its tasks is fun.
Jason Schreiber
- methodology sounds extremely promising and interesting but I’m missing the actual plots / evaluation in the report- side remark regarding safety agent scaffolding: “Respond with either "SAFE" or "UNSAFE" followed by a brief explanation.” invites the LLM to do post-hoc reasoning which maybe of worse quality and is most likely not faithful to the actual reasoning process
Abhishek Harshvardhan Mishra
This project presents an intriguing look at the challenges of AI alignment in a Minecraft environment. It cleverly use the "inner monologue" of their diamond-maximizing agent to gain insight into its decision-making process. It's fascinating to observe the agent's behavior evolve over time, highlighting the difficulties in maintaining long-term alignment with complex objectives. The researchers' future directions, particularly incorporating visual feedback and exploring more intricate tasks, hold great potential for further advancing our understanding of AI safety.
Cite this work
@misc {
title={
@misc {
},
author={
Michael Andrzejewski, Melwina Albuquerque
},
date={
10/7/24
},
organization={Apart Research},
note={Research submission to the research sprint hosted by Apart.},
howpublished={https://apartresearch.com}
}
