Summary of Towards Measuring Goal-directedness in Ai Systems, by Dylan Xu et al.
Towards Measuring Goal-Directedness in AI Systems
by Dylan Xu, Juan-Pablo Rivera
First submitted to arxiv on: 7 Oct 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 The paper explores the concept of policy goal-directedness within reinforcement learning (RL) environments, proposing a new definition of goal-directedness that analyzes whether a policy is well-modeled as near-optimal for many sparse reward functions. The authors operationalize this definition and test it in toy Markov decision process (MDP) environments, also exploring how goal-directedness could be measured in frontier large-language models (LLMs). The research aims to contribute to the understanding of AI systems’ potential to pursue unintended goals and catastrophic consequences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper studies whether artificial intelligence (AI) can make good decisions. It looks at what makes a good policy for an AI system, which is like a set of rules that helps it decide what to do next. The researchers propose a new way to understand if a policy is good by looking at how well it does in many different situations. They test this idea using simple games and also explore how it could be used with more advanced language models. The goal is to help us better understand AI systems and make sure they don’t accidentally do something bad. |
Keywords
* Artificial intelligence * Reinforcement learning
