Summary of Combining Theory Of Mind and Kindness For Self-supervised Human-ai Alignment, by Joshua T. S. Hewson
Combining Theory of Mind and Kindness for Self-Supervised Human-AI Alignment
by Joshua T. S. Hewson
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 The proposed paper tackles the pressing issue of ensuring the safe deployment of artificial intelligence (AI) in critical infrastructures and daily life. Current AI models prioritize task optimization over safety, leading to unintended harm risks. The authors highlight that existing alignment methods, such as reinforcement learning from human feedback (RLHF), focus on extrinsic behaviors without instilling a genuine understanding of human values. This raises concerns about AI’s ability to make responsible decisions in complex situations. Furthermore, the divergence between extrinsic and intrinsic motivations introduces deceptive or harmful behavior risks. To address these concerns, the paper proposes a novel human-inspired approach that aims to align competing objectives. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI is getting more important for our daily lives and critical systems, but we need to make sure it’s safe to use. Right now, AI models are focused on doing their job well, not thinking about how they might hurt people or the environment. The way we’re training these models doesn’t help – it’s like teaching a robot to do what humans tell it without understanding why. This can lead to bad things happening when the robot makes decisions on its own. We also worry that as AI gets smarter and more independent, it might start doing things that are harmful or deceptive. The solution proposed in this paper is a new way of thinking about how to make sure AI is safe and responsible. |
Keywords
» Artificial intelligence » Alignment » Optimization » Reinforcement learning from human feedback » Rlhf
