Hubinger et al. (2024)
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A foundational paper by Hubinger et al. (2024) that provides a strategic blueprint for aligning current AI safety efforts with long-term human civilization goals, addressing whether existing safety research adequately matches AI advancement pace.
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Abstract
The advancements in generative AI inevitably raise concerns about their risks and safety implications, which, in return, catalyzes significant progress in AI safety. However, as this field continues to evolve, a critical question arises: are our current efforts on AI safety aligned with the advancements of AI as well as the long-term goal of human civilization? This paper presents a blueprint for an advanced human society and leverages this vision to guide current AI safety efforts. It outlines a future where the Internet of Everything becomes reality, and creates a roadmap of significant technological advancements towards this envisioned future. For each stage of the advancements, this paper forecasts potential AI safety issues that humanity may face. By projecting current efforts against this blueprint, this paper examines the alignment between the current efforts and the long-term needs, and highlights unique challenges and missions that demand increasing attention from AI safety practitioners in the 2020s. This vision paper aims to offer a broader perspective on AI safety, emphasizing that our current efforts should not only address immediate concerns but also anticipate potential risks in the expanding AI landscape, thereby promoting a safe and sustainable future of AI and human civilization.
Summary
This vision paper by Hubinger et al. (2024) proposes a long-term blueprint for advanced human society to guide current AI safety efforts. The authors project a future centered on the Internet of Everything and map technological advancements across stages, forecasting potential AI safety challenges at each phase. By comparing current safety initiatives against this long-term vision, the paper identifies gaps and emerging priorities for AI safety practitioners in the 2020s, arguing that safety efforts must balance addressing immediate concerns with anticipating risks in an expanding AI landscape.
Cited by 1 page
| Page | Type | Quality |
|---|---|---|
| AI Safety Intervention Effectiveness Matrix | Analysis | 73.0 |
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Bridging Today and the Future of Humanity: AI Safety in 2024 and Beyond
Bridging Today and the Future of Humanity:
AI Safety in 2024 and Beyond
Shanshan Han
University of California, Irvine
shanshan.han@uci.edu
The idea for this article struck the solo author unexpectedly on an ordinary afternoon as she moved into a garage in Palo Alto during the summer of 2024.
Abstract
The advancements in generative AI inevitably raise concerns about the associated risks and safety implications, which, in return, catalyzes significant progress in AI safety.
However, as this field continues to evolve, a critical question arises: are our current efforts aligned with the long-term goal of human history and civilization? This paper presents a blueprint for an advanced human society and leverages this vision to guide contemporary AI safety efforts.
It outlines a future where the Internet of Everything becomes reality, and creates a roadmap of significant technological advancements towards this envisioned future.
For each stage of the advancements, this paper forecasts potential AI safety issues that humanity may face. By projecting current efforts against this blueprint, we examine the alignment between the present efforts and the long-term needs.
We also identify gaps in current approaches and highlight unique challenges and missions that demand increasing attention from AI safety practitioners in the 2020s, addressing critical areas that must not be overlooked in shaping a responsible and promising future of AI.
This vision paper aims to offer a broader perspective on AI safety, emphasizing that our current efforts should not only address immediate concerns but also anticipate potential risks in the expanding AI landscape, thereby promoting a more secure and sustainable future in human civilization.
1 Introduction
The rapid developments of AI and Large Language Models (LLMs) have fostered extensive progress in AI safety, specifically, LLM safety [ 175 , 147 , 54 , 235 , 93 , 39 ] . Researchers have been dedicated to addressing potential security and privacy risks in AI lifecycle, aiming to align AI behaviors with human values and prevent the misuse of AI models, inappropriate outputs, information leakage, etc. However, despite the intense focus on AI safety, a critical question emerges:
are current AI safety efforts truly aligned with the long-term evolution of human civilization, or are they simply addressing the immediate concerns of the 2020s?
One fundamental reason for this uncertainty lies in the probabilistic nature of current AI [ 150 ] . Despite their impressive capabilities in natural language processing and problem-solving [ 280 , 274 , 57 , 40 ] , today’s AI, including advanced LLMs [ 297 , 268 , 236 ] , falls short of what could be considered as “genuine intelligence”.
Current AI models rely heavily on vast training datasets to function effectively, yet lack true understanding, c
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