Summary of Towards Guaranteed Safe Ai: a Framework For Ensuring Robust and Reliable Ai Systems, by David “davidad” Dalrymple and Joar Skalse and Yoshua Bengio and Stuart Russell and Max Tegmark and Sanjit Seshia and Steve Omohundro and Christian Szegedy and Ben Goldhaber and Nora Ammann and Alessandro Abate and Joe Halpern and Clark Barrett and Ding Zhao and Tan Zhi-xuan and Jeannette Wing and Joshua Tenenbaum

Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems

by David “davidad” Dalrymple, Joar Skalse, Yoshua Bengio, Stuart Russell, Max Tegmark, Sanjit Seshia, Steve Omohundro, Christian Szegedy, Ben Goldhaber, Nora Ammann, Alessandro Abate, Joe Halpern, Clark Barrett, Ding Zhao, Tan Zhi-Xuan, Jeannette Wing, Joshua Tenenbaum

First submitted to arxiv on: 10 May 2024

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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 introduces the concept of Guaranteed Safe (GS) AI, which focuses on producing AI systems with high-assurance quantitative safety guarantees. To achieve this, GS AI relies on three core components: a world model that describes how the system affects the outside world, a safety specification that outlines acceptable effects, and a verifier that provides an auditable proof certificate ensuring the system meets the safety specification. The paper presents various approaches for creating these components, highlights technical challenges, and proposes potential solutions. Moreover, it argues for the necessity of GS AI and critiques alternative approaches.
Low	GrooveSquid.com (original content)	Low Difficulty Summary AI systems need to be safe and reliable, especially when they have autonomy or are used in critical situations. This research explores a new approach called Guaranteed Safe (GS) AI. The idea is to make sure AI systems follow safety rules by using three important parts: a world model that shows how the system affects things outside, a safety specification that says what’s allowed, and a verifier that checks if the system follows the rules. The paper talks about different ways to create these parts, what challenges they face, and possible solutions. It also explains why this approach is necessary and what’s wrong with other ideas.