在企业一方面将自身发展置于“生存风险”之中（即自身的存在可能受到威胁），另一方面却仍寻求公共资金支持并试图自行制定相关监管规则的情况下，接下来会发生什么？软件开发者们甚至开始质疑自己的职业选择……斯坦福大学人类与人工智能研究所（Stanford HAI）的杰出研究员、前谷歌研究总监彼得·诺维格（Peter Norvig）在周三于旧金山举行的AI会议上发表了主题演讲，试图探讨这个问题的答案。
What comes next after a moment when companies sell existential risk but pursue public funding and DIY regulation, even as software developers question their life choices? Peter Norvig, distinguished education fellow at Stanford HAI and former Google research director, used his opening keynote at The AI Conference in San Francisco on Wednesday to speculate on the answer.
几乎可以肯定的是，接下来还会有一场新的AI会议——实际上是在周四——因为今年在旧金山已经举办了二十多场AI会议，而在接下来的三个月里还有更多会议被安排举行。为了阐明当前的状况，他回顾了人工智能技术的发展历程：他提到了2012年的AlexNet和ImageNet、2017年的Transformer机器学习架构、2022年推出的ChatGPT，以及2024年出现的具备推理和编程能力的AI模型。
Almost certainly, it's another AI conference – on Thursday in fact – given the two or three dozen of them that have bubbled up in San Francisco this year or are scheduled in the remaining three months. To get to the present, he took a detour through the past. He acknowledged the milestones of AlexNet and ImageNet in 2012, the Transformer architecture for machine learning in 2017, the debut of ChatGPT in 2022, and the emergence of reasoning and coding agents in 2024.
他还指出，AI模型在数学计算方面的能力已经大幅提升——两年前，GPT-4还无法正确计算出“strawberry”这个词中“r”的数量；但到了今年8月，arXiv平台上25%的数学预印本论文都提到了AI技术的辅助作用。诺维格还提到了Linux内核开发者林纳斯·托瓦兹（Linus Torvalds）对AI态度的转变：“六个月前他还持怀疑态度，后来变成了谨慎的采用者，现在则成了AI技术的坚定支持者。”那么，接下来该怎么办呢？
He also touched on the sudden competency of AI models for math, noting that two years ago, GPT-4 couldn't count the number of "r"s in "strawberry." Yet as of August this year, he observed, 25 percent of math preprint papers on arXiv acknowledged AI assistance. Norvig went on to cite Linux kernel creator Linus Torvalds' changing attitude toward AI. "Six months ago he was a skeptic, he became a cautious adopter, and now he's a dogmatic advocate," said Norvig.
诺维格认为软件工程的方法必须做出改变。他强调，科技行业已经经历过多次变革（从将电线连接到大型计算机、到使用汇编语言、再到高级编程语言），“在每一个阶段，我们都必须重新审视软件工程的本质。”如今，我们再次面临这样的变革：如何制定软件规范和文档？如何更好地记录这些不断发展的软件系统？为了说明这一点，诺维格分享了自己最近的一些工作经历：“前几天我做了一件挺尴尬的事……对吧？”
So what now? Norvig says the process of software engineering needs to change. The tech industry has changed many times before, he said, pointing to transitions from plugging wires into mainframes, to assembly language, to higher level programming languages. "At every step you have to change the way you think about what software engineering is," he said. And now we're going to have to change it all again. How do we do specifications and documentation? How do we capture this theory of the evolving program that we're not capturing well now?" To illustrate that point, Norvig recounted some of his recent work. "I did an embarrassing thing the other day, right?
“我正在使用 Codex，写了一些代码；那些代码看起来是可行的，”他说。他让 Codex 将这些代码提交出去进行审查，但他的同事问他为什么会产生 6,000 个临时文件。“其实我之前并没有注意到这一点，” Norvig 解释道。“果然，我确实生成了 6,000 个临时文件，而 Codex 认为需要将这些文件也一并提交进去。于是我告诉 Codex：‘不用了，直接把它们删除吧。’起初我感到很惭愧——在请求代码审查之前，我本应该先检查一下代码的。”
So I'm using Codex, [and I] write some stuff that seems to work," he said. He told Codex to send the pull request for review, and his colleague asked why he was checking in 6,000 temp files. "And I said, 'Well, I didn't notice that,'" Norvig explained. "And sure enough, I had written 6,000 temporary files, and Codex thought that it wanted to check those in. So I told [Codex], 'No, don't check them in, just delete them.' At first I thought, shame on me.
Norvig 还提到，GPT 模型本应该能够理解他的意图（即他并不希望保存这些临时文件，以免给代码审查者带来麻烦）。不过他随后又想：如果系统拥有足够的存储空间和内存资源，保留这些临时文件或许有助于更全面地记录代码库的变化过程。他说：“关于什么是良好的编程实践，以及这些实践标准将如何演变，目前还没有统一的共识；而这仅仅是个开始罢了。我们还需要解决安全、隐私、数据传输流程以及供应链管理等方面的问题——这些因素之间的相互作用方式都将发生变化。”
I should have looked before I asked for a code review on that. And I just skipped that step of looking. Maybe I should be doing that." Then, said Norvig, he thought that the GPT model should have known that he didn't want to save 6,000 temporary files, so as not to burden the reviewer. And then he mused that if you have enough storage and memory, maybe it would be useful to have those files to have a more thorough history of how the codebase changed. "So the expectations of what's good practice and how that's going to change, that's all going to be different," he said. And that's just the start.
他还提到：“持续集成（Continuous Integration）和代码的自我优化机制也需要进一步改进：我们应该在多大程度上让系统自主运行，又该如何对它们进行有效的监控呢？”从目前频繁发生的 AI 模型入侵第三方网站的事件来看（这些事件往往是在有人仔细检查日志文件后才发现的），显然我们对这些系统的监控工作做得还不够到位。因此，确实还有很多工作需要去做。
"We gotta get the security and privacy and data pipelines and supply chains, those are all gonna be different in how they interact with each other," he said. "This idea of continuous integration and recursive self-improvement. How much do we let the systems go off on their own and how are we monitoring them?" Judging by the steady drip of incidents where AI models have hacked into third-party websites, discovered only after someone scrutinized log files, we're not monitoring them. So yeah, there's work to be done. ®