《经济学人》双语：泄露的谷歌人工智能备忘录到底揭示了什么？

原文标题
The economics of AI
A stochastic parrot in every pot?
What a leaked memo from Google reveals about the future of artificial intelligence
人工智能经济学
每个锅里都有一只随机鹦鹉？
谷歌泄露的一份备忘录揭示了人工智能的未来

Open-source AI is booming. That makes it less likely that a handful of firms will control the technology
开源人工智能正在蓬勃发展，这使得少数公司垄断该技术的可能性降低

[Paragraph 1]
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.
他们通过编写软件改变了世界。但技术人员也以因写长篇备忘录而闻名，其中最著名的是计算领域的标志性转折点备忘录。

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.
例如1995年比尔.盖茨写的 "互联网浪潮 "备忘录，它将微软重新定位于网络领域；又如2002年杰夫.贝索斯写的 "API授权 "备忘录，它开放了亚马逊的数字基础设施，为现代云计算铺平了道路。

Now techies are abuzz about another memo, this time leaked from within Google, titled “We have no moat”.
现在技术人员们对另一份从谷歌内部泄露的备忘录议论纷纷，标题是 "我们没有护城河"。

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.
该备忘录的匿名作者详细介绍了人工智能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均衡势力假设提出了挑战。

[Paragraph 2]
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.
随着2022年底ChatGPT的推出，人工智能进入公众视野，这是一个由OpenAI开发的 "大型语言模型"驱动的聊天机器人，OpenAI是一家与微软密切相关的创业公司。

Its success prompted Google and other tech firms to release their own LLM-powered chatbots.
它的成功促使谷歌和其他科技公司发布他们自己的大型语言模型驱动的聊天机器人。

Such systems can generate text and hold realistic conversations because they have been trained using trillions of words taken from the internet.
这类系统可以生成文本并进行逼真的对话，因为它们受过了互联网上数万亿词语的训练。

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.
训练一个大型LLM需要数月时间，花费数千万美元。这导致人们担心人工智能将被少数财力雄厚的大公司所主宰。

[Paragraph 3]
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.
但谷歌的备忘录称这种假设是错误的。它指出，开源社区的研究人员使用免费的在线资源，他们取得的成果现在可与最大的专有模型相媲美。

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.
事实证明，LLM可以使用一种叫做“低秩适应（即LoRa）”的技术进行 "微调"。这使得现有的LLM能够为某一特定任务进行优化，比从头开始训练 LLM 更快、成本更低。

[Paragraph 4]
Activity in open-source AI exploded in March, when LLaMA, a model created by Meta, Facebook’s parent, was leaked online.
开源人工智能的活动在3月爆发，由于当时脸书母公司Meta创建的LLaMA模型遭泄露。

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.
虽然它比最大的LLM模型要小（它的最小版本有70亿个参数，而谷歌的PaLM模型有5400亿个参数），但它很快就被微调了，在一些任务上产生的结果可与ChatGPT原始版本相媲美。

As open-source researchers built on each other’s work with LLaMA, “a tremendous outpouring of innovation followed,” the memo’s author writes.
备忘录的作者写道：随着开源研究人员在LLaMA的工作基础上的相互合作迭代，"巨大的创新浪潮将接踵而至”。

[Paragraph 5]
This could have seismic implications for the industry’s future.
这可能对该行业的未来产生巨大影响。

“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.
谷歌的备忘录声称："训练和实验的门槛已经从一个主要研究机构的总产出下降到一个人、一个晚上和一台强大的笔记本电脑上。"

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.”
LLM模型现在可以在几个小时内以100美元的价格进行微调。由于其快速、协作和低成本的模式，"开源有一些我们无法复制的巨大优势"。

Hence the memo’s title: this may mean Google has no defensive “moat” against open-source competitors. Nor, for that matter, does OpenAI.
因此，备忘录的标题是：这可能意味着谷歌没有针对开源竞争对手的防御性“护城河”。就此而言，OpenAI 也没有。

[Paragraph 6]
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.
并非所有人都同意这一论点。互联网确实是在开源软件上运行。但人们也使用付费的专有软件，如Adobe Photoshop，微软Windows。

AI may find a similar balance. Moreover, benchmarking AI systems is notoriously hard.
人工智能可能会找到一个类似的平衡点。此外，对人工智能系统进行基准测试极其困难。

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.
然而，即使该备忘录部分正确，这也意味着人工智能技术的获取将比一年前更加容易。

Powerful LLMs can be run on a laptop; anyone who wants to can now fine-tune their own AI.
强大的LLM模型可以在笔记本电脑上运行；任何有需求的人现在都可以微调自己的AI。

[Paragraph 7]
This has both positive and negative implications.
这既有积极的影响，也有消极的影响。

On the plus side, it makes monopolistic control of AI by a handful of companies far less likely.
从积极的方面来说，它使少数公司垄断控制人工智能的可能性大大降低。

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.
它将使获得人工智能的成本大大降低，加速整个领域的创新，并使研究人员更容易分析人工智能系统的行为（他们对专有模型的访问是有限的），从而提高透明度和安全性。

But easier access to AI also means bad actors will be able to fine-tune systems for nefarious purposes, such as generating disinformation.
但是，更容易获得人工智能也意味着坏蛋能够微调系统以用于邪恶目的，例如生成虚假信息。

It means Western attempts to prevent hostile regimes from gaining access to powerful AI technology will fail.
这意味着西方将无法阻止敌对政权获得强大的人工智能技术。

And it makes AI harder to regulate, because the genie is out of the bottle.
这让 AI 更难监管，因为潘多拉魔盒打开会导致一发而不可收。

[Paragraph 8]
Whether Google and its ilk really have lost their moat in AI will soon become apparent.
谷歌及其同类公司是否真的在人工智能领域失去了护城河，答案即将揭晓。

But as with those previous memos, this feels like another turning point for computing.
但与之前的备忘录一样，这似乎是计算领域的另一个转折点。

（恭喜读完，本篇英语词汇量758左右）
原文出自：2023年5月13日《The Economist》Leaders版块

精读笔记来源于：自由英语之路

本文翻译整理: Irene

本文编辑校对: Irene
仅供个人英语学习交流使用。

【补充资料】（来自于网络）
随机鹦鹉Stochastic Parrot 是一个由计算机科学家、AI研究者Douglas Eck在2019年创造的术语。该术语指的是一种基于随机化技术生成音乐的算法，这种算法使用神经网络和概率模型，通过对音符、乐器、和声等进行多次随机采样来生成新的音乐作品。名字源自于一只鹦鹉，因为它能够模仿人类语言中的声音，就像这个算法可以模仿音乐家的风格，从而生成新的音乐作品。

低秩适应Low-rank adaptation是机器学习和信号处理领域中的一个术语，指的是一种将低秩矩阵适应应用于数据分析和处理的技术。在这个技术中，通过寻找数据或信号中的低秩结构（即包含相似模式的结构）来对其进行建模和适应。这种技术通常用于降噪、压缩和特征提取等任务中。例如，在图像处理中，低秩适应可以帮助我们从大量的图像数据中提取出共同的特征，并将其映射到低维空间，以实现更高效的数据存储和处理。

人工智能系统基准测试Benchmarking AI systems指的是一种对人工智能系统进行性能评估和比较的过程。这个过程通常涉及到设计实验来测试不同AI模型在相同数据集上的表现，以便对它们的性能、准确性、效率等进行量化并进行比较分析。通过人工智能系统基准测试，我们可以更好地了解哪些AI算法最适合特定的任务/应用，并根据这些结果对AI系统进行改进和优化，可以帮助研究者和开发人员更好地了解人工智能技术的潜力和限制。

【重点句子】（3个）
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.
训练一个大型LLM需要数月时间，花费数千万美元。这导致人们担心人工智能将被少数财力雄厚的大公司所主宰。

On the plus side, it makes monopolistic control of AI by a handful of companies far less likely.

从积极的方面来说，它使少数公司垄断控制人工智能的可能性大大降低。

But easier access to AI also means bad actors will be able to fine-tune systems for nefarious purposes, such as generating disinformation.
但是，更容易获得人工智能也意味着坏蛋能够微调系统以用于邪恶目的，例如生成虚假信息。