The landscape of software development has witnessed a paradigm shift with the
advent of AI-powered assistants, exemplified by GitHub Copilot. However,
existing solutions are not leveraging all the potential capabilities available
in an IDE such as building, testing, executing code, git operations, etc.
Therefore, they are constrained by their limited capabilities, primarily
focusing on suggesting code snippets and file manipulation within a chat-based
interface. To fill this gap, we present AutoDev, a fully automated AI-driven
software development framework, designed for autonomous planning and execution
of intricate software engineering tasks. AutoDev enables users to define
complex software engineering objectives, which are assigned to AutoDev's
autonomous AI Agents to achieve. These AI agents can perform diverse operations
on a codebase, including file editing, retrieval, build processes, execution,
testing, and git operations. They also have access to files, compiler output,
build and testing logs, static analysis tools, and more. This enables the AI
Agents to execute tasks in a fully automated manner with a comprehensive
understanding of the contextual information required. Furthermore, AutoDev
establishes a secure development environment by confining all operations within
Docker containers. This framework incorporates guardrails to ensure user
privacy and file security, allowing users to define specific permitted or
restricted commands and operations within AutoDev. In our evaluation, we tested
AutoDev on the HumanEval dataset, obtaining promising results with 91.5% and
87.8% of Pass@1 for code generation and test generation respectively,
demonstrating its effectiveness in automating software engineering tasks while
maintaining a secure and user-controlled development environment.

AutoDev 是一个全自动化的 AI 驱动的软件开发框架，旨在实现复杂软件工程任务的自主规划和执行，通过自动化的方式执行任务并确保安全和用户控制的开发环境。

AutoDev：自动化 AI 驱动的开发

AutoDev: Automated AI-Driven Development

Governments are increasingly considering integrating autonomous AI agents in
high-stakes military and foreign-policy decision-making, especially with the
emergence of advanced generative AI models like GPT-4. Our work aims to
scrutinize the behavior of multiple AI agents in simulated wargames,
specifically focusing on their predilection to take escalatory actions that may
exacerbate multilateral conflicts. Drawing on political science and
international relations literature about escalation dynamics, we design a novel
wargame simulation and scoring framework to assess the escalation risks of
actions taken by these agents in different scenarios. Contrary to prior
studies, our research provides both qualitative and quantitative insights and
focuses on large language models (LLMs). We find that all five studied
off-the-shelf LLMs show forms of escalation and difficult-to-predict escalation
patterns. We observe that models tend to develop arms-race dynamics, leading to
greater conflict, and in rare cases, even to the deployment of nuclear weapons.
Qualitatively, we also collect the models' reported reasonings for chosen
actions and observe worrying justifications based on deterrence and
first-strike tactics. Given the high stakes of military and foreign-policy
contexts, we recommend further examination and cautious consideration before
deploying autonomous language model agents for strategic military or diplomatic
decision-making.

通过对多个人工智能自主代理在模拟战争游戏中的行为进行研究，我们发现大型语言模型存在升级行动和难以预测升级模式的问题，这可能导致更大的冲突和核武器的使用，因此在部署自主语言模型代理进行战略决策之前需要进一步审查和谨慎考虑。

军事和外交决策中的语言模型升级风险

Escalation Risks from Language Models in Military and Diplomatic  Decision-Making

In a digital epoch where cyberspace is the emerging nexus of geopolitical
contention, the melding of information operations and Large Language Models
(LLMs) heralds a paradigm shift, replete with immense opportunities and
intricate challenges. As tools like the Mistral 7B LLM (Mistral, 2023)
democratise access to LLM capabilities (Jin et al., 2023), a vast spectrum of
actors, from sovereign nations to rogue entities (Howard et al., 2023), find
themselves equipped with potent narrative-shaping instruments (Goldstein et
al., 2023). This paper puts forth a framework for navigating this brave new
world in the "ClausewitzGPT" equation. This novel formulation not only seeks to
quantify the risks inherent in machine-speed LLM-augmented operations but also
underscores the vital role of autonomous AI agents (Wang, Xie, et al., 2023).
These agents, embodying ethical considerations (Hendrycks et al., 2021), emerge
as indispensable components (Wang, Ma, et al., 2023), ensuring that as we race
forward, we do not lose sight of moral compasses and societal imperatives.
Mathematically underpinned and inspired by the timeless tenets of
Clausewitz's military strategy (Clausewitz, 1832), this thesis delves into the
intricate dynamics of AI-augmented information operations. With references to
recent findings and research (Department of State, 2023), it highlights the
staggering year-on-year growth of AI information campaigns (Evgeny Pashentsev,
2023), stressing the urgency of our current juncture. The synthesis of
Enlightenment thinking, and Clausewitz's principles provides a foundational
lens, emphasising the imperative of clear strategic vision, ethical
considerations, and holistic understanding in the face of rapid technological
advancement.

在数字时代中，信息操作和大语言模型的融合标志着一个充满机遇和挑战的新时代。本文提出了一个用于应对这个全新世界的框架，探讨了人工智能增强信息操作的复杂动态，重视自主 AI 代理的道德考虑，并强调清晰的战略愿景、伦理考虑和整体理解在快速技术进步面前的重要性。