This article critically examines the recent hype around AI safety. We first
start with noting the nature of the AI safety hype as being dominated by
governments and corporations, and contrast it with other avenues within AI
research on advancing social good. We consider what 'AI safety' actually means,
and outline the dominant concepts that the digital footprint of AI safety
aligns with. We posit that AI safety has a nuanced and uneasy relationship with
transparency and other allied notions associated with societal good, indicating
that it is an insufficient notion if the goal is that of societal good in a
broad sense. We note that the AI safety debate has already influenced some
regulatory efforts in AI, perhaps in not so desirable directions. We also share
our concerns on how AI safety may normalize AI that advances structural harm
through providing exploitative and harmful AI with a veneer of safety.

人工智能安全性的炒作对于推进社会公益的其他人工智能研究途径产生冲突，因为 AI 安全性与透明度等社会公益相关概念存在微妙而棘手的关系。此外，AI 安全性辩论也可能使一些监管工作朝着不太理想的方向发展，同时给会造成结构性伤害的 AI 提供一种贴上安全标签的机会。

AI 安全：必要，但不足且可能存在问题

AI Safety: Necessary, but insufficient and possibly problematic

We present this article as a small gesture in an attempt to counter what
appears to be exponentially growing hype around Artificial Intelligence (AI)
and its capabilities, and the distraction provided by the associated talk of
science-fiction scenarios that might arise if AI should become sentient and
super-intelligent. It may also help those outside of the field to become more
informed about some of the limitations of AI technology. In the current context
of popular discourse AI defaults to mean foundation and large language models
(LLMs) such as those used to create ChatGPT. This in itself is a
misrepresentation of the diversity, depth and volume of research, researchers,
and technology that truly represents the field of AI. AI being a field of
research that has existed in software artefacts since at least the 1950's. We
set out to highlight a number of limitations of LLMs, and in so doing highlight
that harms have already arisen and will continue to arise due to these
limitations. Along the way we also highlight some of the associated risks for
individuals and organisations in using this technology.

本文介绍了人工智能（AI）领域中的基础和大型语言模型（LLMs）等技术的限制和风险，并且警告使用这些技术会带来的伤害。

大型语言模型的局限性、危害和风险的放大

Amplifying Limitations, Harms and Risks of Large Language Models

Language models with the Transformers structure have shown great performance
in natural language processing. However, there still poses problems when
fine-tuning pre-trained language models on downstream tasks, such as
over-fitting or representation collapse. In this work, we propose HyPe, a
simple yet effective fine-tuning technique to alleviate such problems by
perturbing hidden representations of Transformers layers. Unlike previous works
that only add noise to inputs or parameters, we argue that the hidden
representations of Transformers layers convey more diverse and meaningful
language information. Therefore, making the Transformers layers more robust to
hidden representation perturbations can further benefit the fine-tuning of PLMs
en bloc. We conduct extensive experiments and analyses on GLUE and other
natural language inference datasets. Results demonstrate that HyPe outperforms
vanilla fine-tuning and enhances generalization of hidden representations from
different layers. In addition, HyPe acquires negligible computational
overheads, and is better than and compatible with previous state-of-the-art
fine-tuning techniques.

本研究提出了一种名为 HyPe 的 Fine-tuning 技术，该技术通过扰动 Transformers 层的隐藏表示来缓解 Fine-tuning 中的过拟合和表示坍塌问题，并在 GLUE 等自然语言推理数据集上进行了实验，结果表明，HyPe 优于基本的 Fine-tuning 技术且不会增加额外的计算成本。