Summary of Possible Principles For Aligned Structure Learning Agents, by Lancelot Da Costa et al.
Possible principles for aligned structure learning agents
by Lancelot Da Costa, Tomáš Gavenčiak, David Hyland, Mandana Samiei, Cristian Dragos-Manta, Candice Pattisapu, Adeel Razi, Karl Friston
First submitted to arxiv on: 30 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Neurons and Cognition (q-bio.NC)
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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 proposes a roadmap for developing scalable aligned artificial intelligence (AI) from first-principle descriptions of natural intelligence. The key is enabling agents to learn a good model of the world, including a good model of human preferences. This involves creating agents that can represent the world and other agents’ world models, which falls under structure learning (also known as causal representation learning). The paper outlines the challenges and principles guiding this research, synthesizing ideas from mathematics, statistics, and cognitive science. It discusses the essential role of core knowledge, information geometry, and model reduction in structure learning, and suggests core structural modules for learning naturalistic worlds. Additionally, it outlines a path toward aligned agents through structure learning and theory of mind, using Asimov’s Laws of Robotics as an illustrative example. The paper concludes by proposing refined approaches to alignment, which can guide the development of aligned AI systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating super smart computers that are good friends with humans. It wants to figure out how these computers can learn and understand the world like we do. To do this, it’s important for the computers to learn about our preferences and what we want them to do. The scientists involved in this research are trying to solve a puzzle called “structure learning” which is all about how computers can represent the world and other computers’ ideas about the world. They’re using math, statistics, and even cognitive science (the study of how humans think) to help them figure it out. It’s like a big puzzle that they’re working on to create super smart computers that are friendly and helpful. |
Keywords
» Artificial intelligence » Alignment » Representation learning
