ai人工智能对白-泄露的谷歌备忘录揭示了人工智能怎样的未来？

What does a leaked Google memo reveal about the future of AI?

泄露的谷歌备忘录揭示了人工智能怎样的未来？

Open-source AI is booming. That makes it less likely that a handful of firms will control the technology

开源人工智能正蓬勃发展，由少数几家公司垄断这项技术已不太可能

THEY HAVE changed the world by writing software. But techy types are also known for composing lengthy memos in prose, the most famous of which have marked turning points in computing. Think of Bill Gates's “Internet tidal wave” memo of 1995, which reoriented Microsoft towards the web; or Jeff Bezos's “API mandate” memo of 2002, which opened up Amazon's digital infrastructure, paving the way for modern cloud computing. Now techies are abuzz about another memo, this time leaked from within Google, titled “We have no moat”. Its unknown author details the astonishing progress being made in artificial intelligence (AI)—and challenges some long-held assumptions about the balance of power in this fast-moving industry.

技术人员通过编写软件改变了世界，他们也同样擅长撰写长篇备忘录文章来秀文字，其中最著名的几篇备忘录标志着计算机时代的一些转折。犹记得比尔·盖茨（Bill Gates）1995年的《互联网浪潮》备忘录，将微软的重心转移到互联网领域；又或是杰夫·贝佐斯（Jeff Bezos）2002年的《API指令》备忘录，向外界开放了亚马逊的数字基础设施，为现代云计算的诞生铺平了道路。现在，科技界对另一份备忘录议论纷纷，这次是从谷歌内部泄露的，标题是《我们没有护城河》，作者未知。该备忘录详细介绍了人工智能(AI)领域正取得的惊人进展——并对这个快速发展的行业中一些长期存在的权力平衡假设提出了挑战。

AI burst into the public consciousness with the launch in late 2022 of ChatGPT, a chatbot powered by a “large language model” (LLM) made by OpenAI, a startup closely linked to Microsoft. Its success prompted Google and other tech firms to release their own LLM-powered chatbots. Such systems can generate text and hold realistic conversations because they have been trained using trillions of words taken from the internet. Training a large LLM takes months and costs tens of millions of dollars. This led to concerns that AI would be dominated by a few deep-pocketed firms.

2022年底ChatGPT横空出世，人工智能一夕之间闯入了公众视野。ChatGPT是一款由大语言模型驱动的聊天机器人，由OpenAI开发而成（这是一家和微软关系密切的初创公司）。ChatGPT的成功促使谷歌和其他科技公司也纷纷发布了自己的大语言模型聊天机器人。经过了互联网上提取的数万亿个词汇的训练，这些系统可以生成文本并进行真实的对话。训练一个大型的大语言模型需要数月时间，耗资数千万美元。人们不禁担心人工智能领域将由少数财力雄厚的公司主导。

But that assumption is wrong, says the Google memo. It notes that researchers in the open-source community, using free, online resources, are now achieving results comparable to the biggest proprietary models. It turns out that LLMs can be “fine-tuned” using a technique called low-rank adaptation, or LoRa. This allows an existing LLM to be optimised for a particular task far more quickly and cheaply than training an LLM from scratch.

但是这种想法是错误的，谷歌备忘录如是说。备忘录指出，借助免费的网络资源，开源社区的研究人员所获得的结果能与最大的专有AI模型相媲美。事实证明，可以使用一种叫作低秩适应(Low-rank Adaptation, LoRa)的技术“微调”大语言模型。这样一来，开发人员可以针对某一特定任务专门优化大语言模型，其速度和成本远远优于从零开展模型训练。

Activity in open-source AI exploded in March, when LLaMa, a model created by Meta, Facebook's parent, was leaked online. Although it is smaller than the largest LLMs (its smallest version has 7bn parameters, compared with 540bn for Google's PaLM) it was quickly fine-tuned to produce results comparable to the original version of ChatGPT on some tasks. As open-source researchers built on each other's work with LLaMa, “a tremendous outpouring of innovation followed,” the memo's author writes.

3月ai人工智能对白，开源AI迎来了爆发式增长。Facebook母公司Meta开发的LLaMa大模型在网上惨遭泄露。虽比最大型的大语言模型要小（LLaMa模型最小的版本有70亿参数，而谷歌的PaLM有5400亿个参数），但经过快速微调，在某些任务上的完成度LLaMa可以与ChatGPT初始版本相匹敌。开源研究者们以LLaMa为基础建构大语言模型，在彼此成果基础上迭代更新进步，“一大波创新成果新将会接踵而至”ai人工智能对白，谷歌备忘录的作者写到。

This could have seismic implications for the industry's future. “The barrier to entry for training and experimentation has dropped from the total output of a major research organisation to one person, an evening, and a beefy laptop,” the Google memo claims. An LLM can now be fine-tuned for $100 in a few hours. With its fast-moving, collaborative and low-cost model, “open-source has some significant advantages that we cannot replicate.” Hence the memo's title: this may mean Google has no defensive “moat” against open-source competitors. Nor, for that matter, does OpenAI.

这可能对人工智能领域的未来产生巨大的影响。谷歌备忘录解释道：“一开始，开展模型训练和试验需要来自大型研究机构的总产出结果，如今只需一人、一晚和一台强大的笔记本电脑。”，如今，只要花上几个小时的时间和100美元的成本就可以微调好一个大型语言模型。凭借其快速迭代、相互协作和成本低的模式，“开源有我们无法复制的明显优势”。从这一角度看，备忘录的标题恰如其分：这可能意味着谷歌没有抵御开源竞争对手的“护城河”，同样地，OpenAI也没有。

Not everyone agrees with this thesis. It is true that the internet runs on open-source software. But people use paid-for, proprietary software, from Adobe Photoshop to Microsoft Windows, as well. AI may find a similar balance. Moreover, benchmarking AI systems is notoriously hard. Yet even if the memo is partly right, the implication is that access to AI technology will be far more democratised than seemed possible even a year ago. Powerful LLMs can be run on a laptop; anyone who wants to can now fine-tune their own AI.

不是所有人都同意这一观点。诚然，互联网的运行依赖于开源软件。但人们也使用付费的专有软件，比如Adobe Photoshop与微软Windows系统。或许，AI领域也能实现这种平衡。此外，对AI系统进行基准测试可谓难如登天。然而，即便谷歌的备忘录只说对了一半，那也意味着相较于一年前，如今的AI技术更加普及。一台笔记本电脑就可以运行强悍的大语言模型。只要你想，任何人都可以微调出自己的AI。

This has both positive and negative implications. On the plus side, it makes monopolistic control of AI by a handful of companies far less likely. It will make access to AI much cheaper, accelerate innovation across the field and make it easier for researchers to analyse the behaviour of AI systems (their access to proprietary models was limited), boosting transparency and safety. But easier access to AI also means bad actors will be able to fine-tune systems for nefarious purposes, such as generating disinformation. It means Western attempts to prevent hostile regimes from gaining access to powerful AI technology will fail. And it makes AI harder to regulate, because the genie is out of the bottle.

这种结果有利有弊。好处是，一些公司垄断AI的可能性大大降低；使用AI的成本减少，并且加快了该领域的创新步伐；研究人员也更容易分析AI系统的行为（以前，研究人员难以访问专有AI模型），这有利于提高系统的透明度与安全性。然而，轻轻松松便能获得AI技术也意味着，居心不良的人也能微调系统，来达成不法的企图，比如炮制虚假信息。此外，西方国家试图阻止敌对政权得到强大AI技术的计划将以失败告终。同时，AI监管也愈发困难，因为覆水难收/因为精灵一旦出了瓶子，就回不去了。

Whether Google and its ilk really have lost their moat in AI will soon become apparent. But as with those previous memos, this feels like another turning point for computing.

谷歌与其同侪是否真的已经失去了AI领域的“护城河”，结果很快揭晓。但正如之前发布的备忘录那样，谷歌的这份备忘录或将成为计算机领域的另一转折点。