AI教授们正在应对学术研究的新现实。
内容总结：
AI学者直面学术研究新现实：资源、资金与方向的多重挑战
上周，在由施密特科学AI2050项目于加州山景城举办的一场学术聚会上，众多顶尖AI研究者齐聚一堂，共同探讨人工智能时代学术研究所面临的严峻现实。该项目由埃里克·施密特和温迪·施密特资助，旨在支持从事AI研究的学者。与会者中既有声名显赫的业界巨擘，也有崭露头角的学术新星。
然而，对于占AI2050成员多数的大学研究者而言，当前处境颇为尴尬。过去四年间，AI研究的重心已围绕大语言模型发生根本性转向，其前沿阵地也从学术机构转移至私营企业。大学根本无力承担训练和运行前沿模型所需的昂贵GPU资源，即便勉强为之，Anthropic和OpenAI等公司也绝不会公开Claude或ChatGPT的内部细节。
加州大学伯克利分校计算机科学教授妮卡·哈格塔拉布在午餐交谈中打了个生动的比方：如今的AI学者就像身处一个私营公司独家掌控基因编辑工具CRISPR的世界的生物学家。前沿实验室之外的专家只能研究ChatGPT和Claude的外部行为表现，却无法对这些工具的设计与训练过程进行任何深入研究，更遑论亲自引导其研发方向。
AI2050项目确实为成员提供了一些可用于购买GPU的经费，多位受访研究者表示这是参与该项目的重大利好。但资金问题依然迫在眉睫，尤其是美国联邦科研经费持续削减的当下。即便是不自行运行本地模型的研究者，为了严谨研究而反复调用OpenAI、Anthropic和谷歌的模型，其高昂的查询成本也令人望而却步。
许多成员因此有意避开能力提升这一赛道，转而聚焦于Anthropic或OpenAI不可能涉足的问题。约翰斯·霍普金斯大学计算机科学教授安贾莉·菲尔德明确表示：“我尽量不研究那些我认为科技公司会解决的问题。”企业必须盈利，那些几乎没有利润前景的研究课题自然不值得投资——尤其当研究结论可能让企业难堪时。菲尔德近期的一项研究发现，语言模型对以女性常用措辞提出的问题，给出的回答远不如对男性措辞那般复杂精细。这种研究显然难以想象会出自Anthropic或OpenAI之手。
此外，还有大量AI学者根本不涉及大语言模型。他们多是为分析数据、做出预测乃至模拟整个物理系统而构建专用AI模型的科学家。这些研究者未必与前沿实验室正面竞争——尽管谷歌DeepMind旗下曾打造出诺贝尔奖级蛋白质结构预测模型的AlphaFold团队上月已解散——但他们同样面临诸多自身挑战。会上多位学者担忧，公众对非大语言模型AI的普遍无知正在影响他们的工作。例如，致力于用专用AI工具应对气候变化的学者，在许多人已将“AI”等同于“耗电大户大语言模型”的舆论环境下，往往难以有效推介自己的研究。
这些挑战正在重塑学术生态版图：近期多位知名学者从大学请假加盟前沿实验室，许多AI2050成员也身兼学界与业界双重职务。而最近半年，又一重威胁浮现——OpenAI的模型已成功解决多项数学领域的真实研究难题，部分专家开始忧虑人类在纯数学领域或将失去未来。一位与会学者坦言，自己非常担心数学同行们的心理健康状况。
不过，前景也并非一片黯淡。一方面，实证科学因数据采集本质上的缓慢特性，其自动化难度可能远超数学。另一方面，也有研究者将AI数学家、AI科学家视为助力而非威胁——卡内基梅隆大学致力于让AI模型运行更快、成本更低的计算机科学家蒂姆·德特默斯便持此观点。他认为AI科学家不会取代人类，反而能让人类科学家效率倍增，使他和同行们有机会去探索那些灵光乍现却苦于无暇顾及的大胆构想。
况且，科学家本就是韧性十足的群体。正是资源受限的困境，反而推动他们另辟蹊径，寻找让模型更小、更高效的全新方法，或是探索全然不同的架构。倘若下一个重大AI突破并非出自某家大公司，而是诞生于某个不起眼的学术实验室，我丝毫不会感到意外。
中文翻译：
AI教授们正在应对学术研究的新现实
在施密特科学AI2050项目的一次聚会上，我看到了学术研究人员如何直面人工智能时代的挑战。
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上周，我驱车前往旧金山以南30英里处，来到加利福尼亚州山景城的一家酒店，与一些世界上最杰出、也最有前途的人工智能研究人员会面。我在一场媒体培训中主持圆桌访谈并发言，该培训是施密特科学AI2050项目聚会的一部分。这个项目由埃里克和温迪·施密特资助，支持从事人工智能相关工作的学者。研究员名单堪称人工智能名人大全，虽然并非所有人都来到了湾区，但每当我转过一个拐角，就会看到一位我以前采访过的科学家，或是我钦佩其研究的科学家。（充分披露：我在2024年获得了由施密特科学资助的科学传播奖项。）
对于构成AI2050群体大多数的大学人工智能研究人员来说，这是一个奇怪的时期。在过去四年里，人工智能研究围绕大型语言模型重新定向，其前沿已从学术机构转移到私营公司。大学根本负担不起训练和运行前沿模型所需的GPU，即使负担得起，Anthropic和OpenAI也不会让任何人看到Claude或ChatGPT的内部细节。
在午餐时的交谈中，加州大学伯克利分校的计算机科学教授妮卡·哈格塔拉布说，如今做一名人工智能学者就像在这样一个世界里做生物学家：私营公司独家控制着基因编辑工具CRISPR。前沿实验室之外的专家可以研究ChatGPT和Claude的行为方式，但无法对这些工具的设计和训练进行任何详细研究，也无法自己引导这种设计或训练。
AI2050项目确实为研究员提供了一些可用于购买GPU的资金，我采访的一些研究人员表示，这是参与该项目的一大好处。但资金仍然是一个紧迫的问题，尤其是在美国联邦科研经费削减的情况下。即使对于不自己运行本地模型的研究人员来说，为了严格研究OpenAI、Anthropic和Google的模型而反复查询它们的成本也可能高得令人望而却步。
许多研究员并不专注于推进能力，而是将注意力投向Anthropic或OpenAI不太可能解决的问题。“我尽量不研究那些我认为科技公司会解决的问题，”约翰斯·霍普金斯大学的计算机科学教授安贾莉·菲尔德说。公司需要赚钱，而那些几乎没有盈利前景的研究问题可能不值得投资——尤其是如果答案可能让公司难堪的话。例如，菲尔德最近进行了一项研究，发现语言模型对以女性比男性更常用的措辞方式提出的提示，给出的回答复杂程度较低。很难想象这种研究会出自Anthropic或OpenAI。
还有一大批完全不使用大型语言模型的人工智能学者。他们中的许多人是科学家，构建专门的AI模型来分析数据、做出有用的预测，甚至模拟整个物理系统。这些研究人员不一定在与前沿实验室竞争——Google DeepMind的AlphaFold团队上个月刚刚解散，该团队构建了获得诺贝尔奖的蛋白质结构预测模型。但他们面临着不少自己的挑战。在聚会上，几位与会者表达了对非大型语言模型AI被广泛忽视如何影响他们工作的担忧。例如，构建专门AI工具来帮助应对气候变化的研究人员，在这么多人相信“AI”就是“耗能巨大的大型语言模型”时，有时很难为自己的工作进行辩护。
所有这些挑战都在改变学术界的格局：几位知名学者最近从大学请假加入前沿实验室，许多AI2050研究员在学术工作之外还兼任行业职位。而在过去六个月里，又出现了另一个威胁。OpenAI的模型解决了许多数学领域的真实研究问题，一些专家担心人类在纯数学领域可能没有未来。我采访的一位研究员表示，她担心数学同行们的心理健康。
但也不全是坏消息。一方面，实证科学可能比数学更难自动化，因为收集数据本质上是一个缓慢的过程。而一些研究人员将AI数学家和AI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蒂姆·德特默斯，他的工作是让AI模型的运行更快、更便宜。德特默斯说，AI科学家不会取代人类。相反，它们可以让人类科学家效率大大提高，让他和同行们有机会去追求那些原本可能永远不会去做的天马行空而富有灵感的想法。
而且科学家是一个坚韧的群体。正是那些让他们无法训练前沿模型的资源限制，也推动他们发现让模型更小、更高效的新方法，或者探索全新的架构。如果下一个重大AI突破不是来自大公司，而是来自一个不起眼的学术实验室，我不会感到惊讶。
深度挖掘
人工智能
一家初创公司声称突破了制约大型语言模型的瓶颈
Subquadratic现已公布了其新模型的更多细节。但仍有人持怀疑态度。
一个根本性缺陷使大型语言模型极易受到攻击
这使得诱骗它们做不该做的事情变得很容易，比如告诉你如何破坏飞机的导航系统。
Anthropic发现了一个隐藏空间，Claude在其中思考概念
一项新技术让该公司能够比以往任何时候都更深入地探究大型语言模型的奇妙运作机制。
Claude Science是Anthropic最新的旗舰产品
该公司正在加大对AI用于科学的投入。
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英文来源：
AI professors are negotiating the new realities of academic research
At a convening for the Schmidt Sciences AI2050 program, I saw how academic researchers are facing up to the challenges of the AI era.
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.)
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.
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. 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. 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.
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.
Deep Dive
Artificial intelligence
A startup claims it broke through a bottleneck that’s holding back LLMs
Subquadratic has now shared more details about its new model. But some are still skeptical.
A fundamental flaw leaves LLMs strikingly vulnerable to attack
It makes it easy to trick them into doing things they shouldn’t, such as telling you how to sabotage an aircraft’s navigation system.
Anthropic found a hidden space where Claude puzzles over concepts
A new technique has let the company probe deeper than ever into the weird workings of an LLM.
Claude Science is Anthropic’s newest flagship product
The company is doubling down on AI for science.
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