This work identifies 18 foundational challenges in assuring the alignment and
safety of large language models (LLMs). These challenges are organized into
three different categories: scientific understanding of LLMs, development and
deployment methods, and sociotechnical challenges. Based on the identified
challenges, we pose $200+$ concrete research questions.

针对大型语言模型（LLMs）的对齐和安全问题，本研究确定了 18 个基础挑战，并将其组织成科学理解、开发和部署方法以及社会技术挑战三个不同类别。根据这些挑战，我们提出了 200 多个具体的研究问题。

大型语言模型对齐与安全的基础挑战

Foundational Challenges in Assuring Alignment and Safety of Large  Language Models

Modern life sciences research is increasingly relying on artificial
intelligence approaches to model biological systems, primarily centered around
the use of machine learning (ML) models. Although ML is undeniably useful for
identifying patterns in large, complex data sets, its widespread application in
biological sciences represents a significant deviation from traditional methods
of scientific inquiry. As such, the interplay between these models and
scientific understanding in biology is a topic with important implications for
the future of scientific research, yet it is a subject that has received little
attention. Here, we draw from an epistemological toolkit to contextualize
recent applications of ML in biological sciences under modern philosophical
theories of understanding, identifying general principles that can guide the
design and application of ML systems to model biological phenomena and advance
scientific knowledge. We propose that conceptions of scientific understanding
as information compression, qualitative intelligibility, and dependency
relation modelling provide a useful framework for interpreting ML-mediated
understanding of biological systems. Through a detailed analysis of two key
application areas of ML in modern biological research - protein structure
prediction and single cell RNA-sequencing - we explore how these features have
thus far enabled ML systems to advance scientific understanding of their target
phenomena, how they may guide the development of future ML models, and the key
obstacles that remain in preventing ML from achieving its potential as a tool
for biological discovery. Consideration of the epistemological features of ML
applications in biology will improve the prospects of these methods to solve
important problems and advance scientific understanding of living systems.

现代生命科学研究越来越依赖于人工智能方法来模拟生物系统，主要集中在使用机器学习模型上。然而，机器学习在生物科学中的广泛应用表明其与传统科学探究方法存在显著差异，这种相互作用对未来的科学研究具有重要意义，但却受到较少关注。在本研究中，我们借鉴认识论工具集，将机器学习在生物科学中的最新应用放入现代哲学理论的框架下，以识别可指导机器学习系统为生物现象建模和推动科学知识进展的一般原则。我们提出，科学理解作为信息压缩、定性可理解性和依赖关系建模的概念，为解释机器学习介导的生物系统理解提供了有用的框架。通过对现代生物研究中机器学习的两个关键应用领域 —— 蛋白质结构预测和单细胞 RNA 测序的详细分析，我们探讨了这些特征迄今如何使机器学习系统推进对目标现象的科学理解，以及它们如何指导未来机器学习模型的发展，以及阻碍机器学习实现其作为生物发现工具潜力的关键障碍。考虑机器学习在生物学应用中的认识论特征将改善这些方法解决重要问题和推进对生命系统科学理解的前景。

人工智能时代的生物学理解

Understanding Biology in the Age of Artificial Intelligence

Scientific understanding is a fundamental goal of science, allowing us to
explain the world. There is currently no good way to measure the scientific
understanding of agents, whether these be humans or Artificial Intelligence
systems. Without a clear benchmark, it is challenging to evaluate and compare
different levels of and approaches to scientific understanding. In this
Roadmap, we propose a framework to create a benchmark for scientific
understanding, utilizing tools from philosophy of science. We adopt a
behavioral notion according to which genuine understanding should be recognized
as an ability to perform certain tasks. We extend this notion by considering a
set of questions that can gauge different levels of scientific understanding,
covering information retrieval, the capability to arrange information to
produce an explanation, and the ability to infer how things would be different
under different circumstances. The Scientific Understanding Benchmark (SUB),
which is formed by a set of these tests, allows for the evaluation and
comparison of different approaches. Benchmarking plays a crucial role in
establishing trust, ensuring quality control, and providing a basis for
performance evaluation. By aligning machine and human scientific understanding
we can improve their utility, ultimately advancing scientific understanding and
helping to discover new insights within machines.

本文提出了一种基于科学哲学的框架，利用行为学概念来评估机器智能和人类的科学理解水平，并通过一组测试建立了科学理解基准，以便评估和比较不同方法和水平。

人类和机器科学理解的基准创建

Towards a Benchmark for Scientific Understanding in Humans and Machines

Imagine an oracle that correctly predicts the outcome of every particle
physics experiment, the products of every chemical reaction, or the function of
every protein. Such an oracle would revolutionize science and technology as we
know them. However, as scientists, we would not be satisfied with the oracle
itself. We want more. We want to comprehend how the oracle conceived these
predictions. This feat, denoted as scientific understanding, has frequently
been recognized as the essential aim of science. Now, the ever-growing power of
computers and artificial intelligence poses one ultimate question: How can
advanced artificial systems contribute to scientific understanding or achieve
it autonomously?
We are convinced that this is not a mere technical question but lies at the
core of science. Therefore, here we set out to answer where we are and where we
can go from here. We first seek advice from the philosophy of science to
understand scientific understanding. Then we review the current state of the
art, both from literature and by collecting dozens of anecdotes from scientists
about how they acquired new conceptual understanding with the help of
computers. Those combined insights help us to define three dimensions of
android-assisted scientific understanding: The android as a I) computational
microscope, II) resource of inspiration and the ultimate, not yet existent III)
agent of understanding. For each dimension, we explain new avenues to push
beyond the status quo and unleash the full power of artificial intelligence's
contribution to the central aim of science. We hope our perspective inspires
and focuses research towards androids that get new scientific understanding and
ultimately bring us closer to true artificial scientists.

本文探讨了人工智能在科学理解方面的贡献，阐述了其作为计算显微镜、灵感资源和未来的理解者三个维度，提出了新的推进人工智能在科学理解中的应用的途径。