Summary of Informal Safety Guarantees For Simulated Optimizers Through Extrapolation From Partial Simulations, by Luke Marks
Informal Safety Guarantees for Simulated Optimizers Through Extrapolation from Partial Simulations
by Luke Marks
First submitted to arxiv on: 29 Nov 2023
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 The paper explores the concept of simulators in self-supervised learning for language modeling. It posits that training with predictive loss on a self-supervised dataset can create simulators, which are entities representing possible configurations of real-world systems. Building upon the Cartesian frames model of embedded agents, the paper develops a mathematical model for simulators in multi-agent worlds through scaling and dimensionality. The proposed framework, called the Cartesian object, represents simulations with individual simulacra as agents and devices. The paper then formalizes the behavior of simulators by accounting for token selection and simulation complexity, demonstrating the Löbian obstacle’s impossibility of proof-based alignment between simulacra. To circumvent this challenge, the paper introduces Partial Simulation Extrapolation (PSE), an evaluation scheme for low-complexity simulations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a special kind of computer program that can learn and improve without being explicitly taught. This “simulator” is like a mini version of our world, where it can test different scenarios and outcomes. The paper explores how simulators work in language modeling, which is the process of training computers to understand and generate human-like text. The researchers create a new framework for building these simulators, allowing them to simulate complex systems with multiple agents working together. However, they also find that it’s impossible to prove that these simulations accurately represent our world. To overcome this challenge, they propose a new method called Partial Simulation Extrapolation, which can help computers learn from low-complexity simulations. |
Keywords
* Artificial intelligence * Alignment * Self supervised * Token
