AI professors are negotiating the new realities of academic research

AI 教授们正在应对学术研究的新现实

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. Last week, I headed 30 miles south of San Francisco to a hotel in Mountain View, California, to join some of the most accomplished, and some of the most promising, AI researchers in the world. I was hosting roundtable interviews and speaking at a media training for a convening of the Schmidt Sciences AI2050 program, an initiative funded by Eric and Wendy Schmidt that supports academics whose work involves AI. The fellows list is a who’s who of AI luminaries, and though not all of them made it out to the Bay, every time I turned a corner I saw a scientist whom I’d interviewed previously or whose research I admired. (Full disclosure: I received a science communication award funded by Schmidt Sciences in 2024.)

本文最初发表于我们的 AI 每周通讯《算法》（The Algorithm）。若想第一时间在收件箱中获取此类报道，请点击此处订阅。上周，我前往旧金山以南 30 英里的加利福尼亚州山景城的一家酒店，与一些世界上最有成就、也最有前途的 AI 研究人员会面。我当时正在主持圆桌采访，并在 Schmidt Sciences AI2050 项目的会议上进行媒体培训演讲。该项目由埃里克·施密特（Eric Schmidt）和温迪·施密特（Wendy Schmidt）资助，旨在支持从事 AI 相关工作的学者。研究员名单汇集了 AI 领域的杰出人物，尽管并非所有人都来到了湾区，但我每转过一个弯，都能看到我曾采访过或其研究令我钦佩的科学家。（披露：我曾于 2024 年获得由 Schmidt Sciences 资助的科学传播奖。）

It’s a weird time for university AI researchers, who make up most of the AI2050 group. In the past four years, AI research has reoriented around large language models, and its cutting edge has moved from academic institutions to private companies. Universities simply can’t afford the GPUs required to train and run frontier models, and even if they could, Anthropic and OpenAI aren’t letting anyone else see the inner details of Claude or ChatGPT. In a conversation over lunch, Nika Haghtalab, a computer science professor at UC Berkeley, said that being an AI academic these days was like being a biologist in a world in which private companies had exclusive control over the gene-editing tool CRISPR. Experts outside the frontier labs can study how ChatGPT and Claude behave, but they can’t do any detailed research on the design and training of those tools, nor can they steer that design or training themselves.

对于构成 AI2050 核心群体的大学 AI 研究人员来说，这是一个尴尬的时期。在过去四年里，AI 研究已转向以大语言模型为中心，其前沿阵地也从学术机构转移到了私营企业。大学根本负担不起训练和运行前沿模型所需的 GPU，即便负担得起，Anthropic 和 OpenAI 也不会让任何人窥探 Claude 或 ChatGPT 的内部细节。在午餐交谈中，加州大学伯克利分校计算机科学教授 Nika Haghtalab 表示，如今做一名 AI 学者，就像是在一个私营公司独家控制基因编辑工具 CRISPR 的世界里做生物学家。前沿实验室之外的专家可以研究 ChatGPT 和 Claude 的行为，但无法对这些工具的设计和训练进行深入研究，也无法主导其设计或训练过程。

The AI2050 program does offer fellows some funding that they can use to buy GPUs, which some researchers I spoke with said was a major benefit of participating in the program. But money remains a pressing concern, especially given the reduction of federal scientific funding in the United States. Even for researchers who don’t run local models themselves, the cost of repeatedly querying OpenAI’s, Anthropic’s, and Google’s models in order to study them rigorously can be prohibitive. Rather than focusing on advancing capabilities, many fellows aim their attention at questions that are unlikely to be addressed by Anthropic or OpenAI. “I try not to work on problems that I think are gonna be solved by a tech company,” says Anjalie Field, a computer science professor at Johns Hopkins.

AI2050 项目确实为研究员提供了一些可用于购买 GPU 的资金，我交谈过的一些研究人员表示，这是参与该项目的一大益处。但资金仍然是一个紧迫的问题，特别是在美国联邦科学经费削减的情况下。即使对于那些不运行本地模型的研究人员来说，为了严谨地研究 OpenAI、Anthropic 和 Google 的模型，反复调用它们所产生的费用也可能高得令人望而却步。许多研究员不再专注于提升模型能力，而是将注意力转向那些 Anthropic 或 OpenAI 不太可能解决的问题。“我尽量不去研究那些我认为会被科技公司解决的问题，”约翰霍普金斯大学计算机科学教授 Anjalie Field 说。

Companies need to make money, and research questions that have little promise of profit might not be worth investing in—especially if their answers might make the companies look bad. Recently, for example, Field conducted a study in which she found that language models give less sophisticated responses to prompts that are phrased in ways more commonly used by women than by men. It’s difficult to imagine that kind of research coming out of Anthropic or OpenAI. There’s also a huge group of AI academics who don’t work with LLMs at all. Many of them are scientists who build specialized AI models that can analyze data, make useful predictions, or even simulate entire physical systems. Those researchers aren’t necessarily competing with the frontier labs—Google DeepMind’s AlphaFold team, which built a Nobel Prize–winning model that predicts the structures of proteins, was disbanded last month. But they face plenty of their own challenges.

公司需要盈利，那些几乎没有利润前景的研究课题可能不值得投资——尤其是当研究结果可能让公司形象受损时。例如，Field 最近进行了一项研究，发现如果提示词的措辞方式更偏向女性常用习惯而非男性，语言模型给出的回答往往不够精细。很难想象 Anthropic 或 OpenAI 会发布此类研究。此外，还有一大批 AI 学者根本不从事大语言模型（LLM）的研究。他们中的许多人是构建专用 AI 模型的科学家，这些模型可以分析数据、做出有用的预测，甚至模拟整个物理系统。这些研究人员并不一定在与前沿实验室竞争——例如，构建了获得诺贝尔奖的蛋白质结构预测模型 AlphaFold 的 Google DeepMind 团队，上个月就被解散了。但他们也面临着许多自身的挑战。

At the convening, several voiced concerns about how the widespread ignorance of non-LLM AI was affecting their work. Researchers who build specialized AI tools to help address climate change, for example, sometimes struggle to advocate for their work when so many people believe that “AI” means “energy-guzzling LLMs.” All these challenges are changing the landscape of academia: Several prominent academics have recently taken leave from their universities to join frontier labs, and many AI2050 fellows hold industry positions alongside their academic jobs. And in the past six months, yet another threat has emerged. OpenAI’s models have solved a number of real research problems in mathematics, and some experts are worried that humans might not have a future in pure math. One fellow I spoke with said that she was concerned about the mental health of her mathematician peers.

在会议上，几位学者表达了担忧，认为大众对非 LLM 类 AI 的普遍无知正在影响他们的工作。例如，那些构建专用 AI 工具以应对气候变化的研究人员，在许多人认为“AI”就等于“耗能巨大的 LLM”时，往往难以推广他们的工作。所有这些挑战正在改变学术界的格局：几位知名学者最近从大学请假加入了前沿实验室，许多 AI2050 研究员在担任学术职务的同时也身兼工业界职位。在过去六个月里，又出现了一个新的威胁。OpenAI 的模型已经解决了一些数学领域的实际研究问题，一些专家担心人类在纯数学领域可能没有未来。我交谈过的一位研究员表示，她很担心数学家同行的心理健康。

But it’s not all doom and gloom. For one thing, empirical science may prove much more difficult to automate than mathematics, because collecting data is an intrinsically slow process. And some researchers see AI mathematicians and scientists as a boon rather than a threat—including Tim Dettmers, a computer scientist at Carnegie Mellon who works to make AI models faster and cheaper to run. AI scientists won’t replace humans, Dettmers says. On the contrary, they could make human scientists far more efficient, so that he and his peers have the chance to pursue all the wild and inspired ideas they might otherwise never have gotten around to. And scientists are a resilient sort. The very resource constraints that prevent them from training frontier models also push them to discover new ways to make models smaller and more efficient, or to explore completely new architectures. If the next big AI breakthrough comes not from a major company but from a scrappy academic lab, I won’t be shocked.

但这并非全是悲观情绪。首先，实证科学可能比数学更难实现自动化，因为收集数据本身就是一个缓慢的过程。一些研究人员将 AI 数学家和科学家视为福音而非威胁——包括卡内基梅隆大学的计算机科学家 Tim Dettmers，他致力于让 AI 模型的运行更快、更便宜。Dettmers 说，AI 科学家不会取代人类。相反，它们可以使人类科学家效率更高，从而让他和同行们有机会去追求那些原本可能永远无法实现的疯狂而灵动的想法。科学家们具有很强的韧性。正是那些阻碍他们训练前沿模型的资源限制，促使他们发现了让模型更小、更高效的新方法，或者去探索全新的架构。如果下一个重大的 AI 突破不是来自大公司，而是来自一个规模较小的学术实验室，我一点也不会感到惊讶。