欢迎收看关于AI研究的通讯《进口AI》 进口AI运行于arXiv,capucinos，以及读者的反馈。 如果你愿意支持
以下是我10月17日星期六在曼谷货币基金组织会议上发表的讲话。 这个演讲的灵感来自大趋势的读者们看到我今年报道AI开始在人类科学前沿运行，有时是自主运行。 虽然我们都可能会在细节上纠缠不休，但我认为，我们显然处于独特性的早期阶段，我想利用我的特权地位，发表Per Jacobsson演讲，试图告诉聚集在一起的经济学家、银行家和决策者们，他们应该期待未来的迅速变化。 在演讲中，我主要关注人类如何分享AI的好处
我在这里极其认真地对待自己的责任。 我发现自己既是一个AI正在发生的事情的旁白者，一个在AI正在发生的事情背后的原则agent，以及一个在许多社区之间展开的翻译。 在所有这些事情中，我力求诚实，并明确自己的责任。 这是一个永远的工作在进行中。
感谢大家的阅读。 这样的演讲大量借鉴了通讯的内容，而通讯是我每周向大家提供信息的尝试。 谢谢你的读者
***
事件地平线内部：为什么每个人都应该认真对待AI进步。
你好，谢谢你邀请我我很荣幸能来到这里。
首先，我应列出我的相关全权证书：我是一个人类。 我有一个我花了将近40年的时间来积累发表这一演讲的经验。 我稍后再谈这些证书。
我的整个职业生涯是通过找到对世界未来似乎很重要的信息，然后向世界介绍这些信息来界定的。 20年前，我开始我的记者生涯，专注于数据中心。 通过这部作品，我变得对AI如此着迷，以至于2016年离开新闻工作到OpenAI工作，几年后又共同创办了Anthropic. 从那时起，我一直试图让世界相信，有朝一日，AI系统可能变得强大到可以改变国家安全，改变地球的经济轨迹，并最终获得建立自己的能力。
时机到了 AI正在改变国家安全，它正在改变世界的经济轨迹，正如我们在很多会议上听到的那样
当然，你们中许多人都知道这一点，你们必须追踪AI，这是影响你们的世界和工作的众多破坏稳定力量之一。
我今天在这里解释的是，大赦国际正在实现比我们所期望的更大的变革，我们应当作相应的准备。 我会解释原因。
多年来，研究人员和科幻小说作者预测未来AI系统会大幅提升整体科技进步的步伐，使事物发展得如此之快，以至于人类难以跟上。
他们称之为奇特。 我认为，今年我们已经获得令人信服的证据，证明它已经开始。
作家弗诺·温格在1993年预测了这一点。 “当比人类情报更强能推动进步时，这种进步将更快。 事实上，进步本身似乎没有理由不涉及建立更聪明的实体 — — 在更短的时间尺度上，“他写道，将这种独特性描述为“必须抛弃我们的模型和新的现实规则”。
奇点在这里。 影响涉及生活的各个方面。 但最重要的含义是简单的：我们应该期望，在未来几年里，不仅AI，而且整个科学的整个进步步伐将会加快。 这将感到困惑和迷惑，并将带来巨大的财富和潜在的巨大危险。
今天，我要集中谈第一点：快速AI进步的潜在好处，以及我们需要共同分享这些好处。
人类从AI中获益的许多方式都来自科学。 我认为AI驱动的科学发明将改变全球经济的走向，改变各国规划和建设自身基础设施的方式，使我们更加健康，并能与亲人相处更多的时间，同时，QQ也促使我们大家就我们应该对它的持续发展作出的选择提出深刻的哲学问题。
我将提出我的主张：近半个世纪以来，大多数国家都在研究生产力下降，而研究人员的人数却迅速增加 -- -- 想法和发明似乎越来越难找到。
但重要的事情已经改变：我们现在已经建立了人工智能系统，这些系统能够增加、扩展和自动化人类劳动力的各个方面。 这在软件开发中最为明显，AI系统现在比世界上绝大多数软件工程师的能力和效率更高。
因此，软件开发领域正在发生变化——一个人现在能够为任何他们能想象的东西写软件，创造出以前会夺走整个人类团队的东西。
在Anthropic内部，我们的研究人员现在生产了近十倍于公司成立前四年的密码。
但新的AI能力带来了新的挑战——编码的改进意味着AI系统在网络安全和网络攻击上有了更好的表现，导致世界竞相部署这些强大的能力来创造更好的网络防御，而坏角色竞相利用这些系统进行黑客攻击。
这里需要注意的是，这些东西是捆绑在一起的，不可分割的——在诸如编码这样的一般领域，AI系统的进步也在诸如网络安全等具有国际意义的领域产生了积极和消极影响。
这是未来事物的味道。 取得的进展产生了新的能力和新的挑战。
这些能力不会停止编码；同样的事情也会发生在科学和机器人身上。 它已经开始发生。 这意味着全球经济的一些重要特性可能会发生变化，比如创新的速度，或者我们发明全新的通用技术的频率，这些技术在自己周围创造了新的产业，比如互联网、电话和人工智能。
那么，是什么让我相信这个？2026年，我们有了第一个迹象，AI系统可能有能力在前沿做科学，我们有一些启发性的例子来说明未来可能是什么样子。 例如：
在数学中，AI系统被用于提出长期数学问题的解决方案，如纳维耶·斯托克斯千年奖，雅各布猜想，以及几个开放的埃尔多斯问题。 在许多情况下，人工智能系统负责核心的洞察力，使解决办法成为可能。
在生物学中，研究人员利用AI从零开始设计病毒，为测试确定新的药物候选者，今天在试验中已经出现了大部分由AI设计的药物。
在机器人领域，AI系统现在越来越能够自主地远程操作实验室设备，使我们能够在从生物学到材料科学的领域进一步实现实验自动化，使电池和燃料电池开发等领域的进展更快。
从个别情况来看，每个例子都令人兴奋。 但深刻的事实是，它们都与同样的基本技术相关，而且对于大多数这些使用案例来说，都是相同的基本前沿模型。 我们现在有单元实体——Claude或ChatGPT或Gemini等AI系统，它们可以在多个科学学科的前沿进行工作。
量化的衡量标准证实了这一点。
一个这样的衡量标准是AI系统的得分
这项命名不祥的试验是针对AI系统在过去几年中在各种困难和多样的科学挑战中取得的深刻进展而制订的，涉及的课题多种多样，如高级化学、计算机科学、数学、古典文学和生态学。
2025年1月的考试发布时，最高评分AI系统获得了8%的分数。 截至本周，评分最高的系统为Claude Opus 5.5，占67.7%.
另一个例子是FrontierMath，这是2024年11月的一次测试发布，它试图评估AI系统如何能很好地解决数百个具有挑战性的数学问题，由专业数学家进行解析和评估。 发布时，最佳评分系统获得2%. 现在，他们得到94%。
更广义地说，它看起来到处都是这样的。 选择你最喜爱的科普内容
西奥普斯瓦尔
我将用一个故事来结束我的证据。 最新的例子来自上周刚刚发表的一篇论文，名为TheoPhysEval。 这是一份测试AI系统能在多大程度上解决理论物理中未公布的问题的论文。 论文中的50个问题由世界各地的物理学家汇编而成。
毫不奇怪，AI系统获得了很高的分数。 但令人惊讶的是，他们是如何获得高分的。 在这里，我要引用印度物理学家苏弗拉特·拉朱(Suvrat Raju)的话，他写了一篇评论结果的文章。
称表示：「我不认识任何个人，" 我们的初步评价表明，GPT-6-Astra的得分是在80年代末，紧随其后的是Opus 5.5。 但是，当我们开始调查模型可能所犯的错误时，模型，特别是阿斯特拉坚持说它们是正确的，而错误的是专家提供的答案。 当我们回到专家面前时，发现专家在很多情况下发现了一些隐藏在他们计算中的一些微妙错误。 Astra的得分开始增加，最终达到98%(49/50)。
考虑一下世界上一些科学专家在问题中做了个测试，而当他们测试AI时，不仅得分高，而且通过发现人类所研究的答案中的错误而得到更高的分数！
电影中有一个场景，通常是经过的三分之二，当年轻的主角最终向导师证明他们有需要去世界独立成功。 当老师成为学生时，这就是这里正在发生的事情。
我为什么要告诉你这些？为什么AI系统在科学上越来越好有关系？
我认为这是因为做真正的前沿科学—— 能够取得突破从而扩展知识的前沿—— 暗含着对AI能力的最后前沿之一的考验：创造力。 我认为，如果人工智能系统能够创造性地解决在科学领域取得成功的问题，我们应当期望这能表明它们有能力将知识概括到许多领域，当然也包括人工智能研究本身，这可能会对创新产生爆炸性影响。
因此，如果AI系统在科学上得到更好的发展，这将有力地推动未来经济的增长和形态，这意味着你今天可以作出决定，建立你的国家和人民，在未来从这一趋势中受益。
科学和增长
让我们谈谈G
本文由 AI宝盒自动抓取并翻译，版权归原作者所有；如需查看原文请点下方链接。
Welcome to Import AI, a newsletter about AI research. Import AI runs on arXiv, cappuccinos, and feedback from readers. If you’d like to support this, please subscribe.
Here’s a speech I gave on Saturday the 17th of October at the IMF meeting in Bangkok. This speech is inspired by the megatrend readers have seen me cover this year of AI beginning to operate at the frontier of human science, sometimes autonomously. While we all may quibble over the details, I think it’s evident we’re in the early stages of the singularity, and I wanted to use my privileged position of giving the Per Jacobsson lecture to try and tell the assembled audience of economists, bankers, and policymakers, that they should expect significant, rapid change in the future. In this speech, I focused primarily on how humanity can share in the benefits of AI if we exercise proper foresight and restraint, and I’m going to be expanding on the stakes of the singularity and the risks that we’ll all face in coming issues.
As I said a few times in the speech - some of which was ad libbed and not captured here - I take my own responsibility here extremely seriously. I find myself both a narrator of what is happening in AI, a principle agent behind what is happening in AI, and a translator that goes between many communities. In all of this, I am seeking to be truthful and to be clear about my own culpability. This is a forever work-in-progress.
Thank you all for reading. Speeches like this draw heavily from the newsletter, and the newsletter is my attempt to provide week-by-week information to all of you. I appreciate your readership.
***
Inside the Event Horizon: Why Everyone Should Take AI Progress Seriously
Hello, thank you for having me. It is a great privilege to be here.
To start, I should list my relevant credentials: I am a human being. There is one of me. It has taken me almost 40 years to build the experience to give this speech. I will come back to those credentials later.
My whole career has been defined by finding information that seemed important to the future of the world, then telling the world about that information.Two decades ago, I started my career as a journalist, focusing on data centers. Through that work, I became so obsessed with AI that in 2016 I left journalism to work at OpenAI, then co-founded Anthropic a few years later. Ever since, I’ve been trying to convince the world that one day, AI systems might become so powerful that they could alter national security, change the economic trajectory of the planet, and perhaps eventually gain the ability to build themselves.
Well, the moment has arrived. AI is now changing national security, it is altering the economic trajectory of the world - as we’ve heard in so many sessions here - and is contributing to its own development.
Of course, many of you know this, and you are having to track AI as one of a multitude of destabilizing forces that is shaping your world and your work.
What I am here to explain today is that AI is on a course to bring about even more change than what we are expecting and we should prepare accordingly. And I’m going to explain why.
For many years, researchers and science fiction authors have forecast a future in which AI systems drastically increase the pace of overall scientific and technical progress, making things move so quickly that it is hard for humans to keep up.
They call this the singularity. I believe that this year we have gained convincing evidence that it has now begun.
The writer Vernor Vinge forecast this in 1993. “When greater-than-human intelligence drives progress, that progress will be much more rapid. In fact, there seems no reason why progress itself would not involve the creation of still more intelligent entities -- on a still-shorter time scale,” he wrote, describing the singularity as “a point where our models must be discarded and a new reality rules.”
The singularity is here. The implications reach across every aspect of life. But the most important implication is a simple one: we should expect that in the coming years, the whole pace of progress of not just AI, but science as a whole, will speed up. This will feel confusing and disorienting, and it will bring with it great wealth and potentially great peril.
Today, I’m going to concentrate on the first point: the potential benefits of rapid AI progress, and what we need to do to share in these benefits together.
So many of the ways that humanity will benefit from AI come from science. I believe that AI-driven scientific invention will change the course of the global economy, alter the ways countries plan and build their own infrastructure, make us all healthier and able to spend more time with our loved ones, and –– at the same time –– cause us all to ask deep philosophical questions about choices we should make about its continued development.
I’ll develop my claim: For the last half century, most countries have dealt with falling research productivity while researcher headcount grows rapidly - ideas and inventions appear to be getting harder to find.
But something important has changed: we’ve now built AI systems which have been able to augment, extend, and automate aspects of human labor. This has most obviously happened in software development, where AI systems are now more capable and efficient than the vast majority of the world’s software engineers.
As a consequence, the field of software development is changing - A single person is now able to write software for anything they can imagine, creating things that previously would have taken entire teams of humans.
We’ve seen concrete evidence of this inside Anthropic, where our researchers now produce almost 10 times the amount of code than they did for the first four years of the company’s life.
But new AI capabilities bring new challenges - the coding improvements have meant AI systems have got much better at cybersecurity and cyberattacks –– leading the world to race to deploy these powerful capabilities to create better cyberdefenses, while bad actors race to use these systems to carry out hacks.
The important thing to notice here is that these things are bound up, inseparably - the advance of AI systems in a general area like coding has also created both positive and negative impacts in domains of international relevance, like cybersecurity.
This is a taste of things to come. Of progress yielding new capabilities, and new challenges.
These capabilities will not stop at coding; the same thing will happen to science and robotics. It has already begun to happen. And it means some important properties of the global economy may change - like the rate of innovation, or the frequency with which we invent entirely new general purpose technologies that create new industries around themselves, like the internet, phones, and artificial intelligence.
So, what causes me to believe this? In 2026, we have got the first signs that AI systems may be capable of doing science at the frontier, and we have some evocative examples of what the future could look like. For instance:
In mathematics, AI systems have been used to propose solutions to longstanding mathematical problems, like the Navier Stokes Millenium Prize, the Jacobian Conjecture, and several open Erdos problems. In many cases, AI systems were responsible for core insights that made the solution possible.
In biology, researchers have used AI to design viruses from scratch, to identify new drug candidates for testing, and there are already drugs in trials today which have been in large part designed by AI.
In robotics, AI systems are now increasingly able to autonomously operate laboratory equipment remotely, making it possible for us to further automate experimentation in domains from biology to materials science, making progress faster in areas like batteries and fuel-cell development.
Taken individually, each of these examples is exciting. But the profound fact is that they all relate to the same underlying technology - and for most of these use-cases, the same single underlying frontier model. We now have singular entities - AI systems like Claude or ChatGPT or Gemini - which can carry out work at the frontier of multiple scientific disciplines.
Quantitative metrics bear this out.
One such metric is the score of AI systems at a test called Humanity’s Last Exam - I didn’t choose the name!
This ominously named test was developed in response to the profound advances seen in prior years by AI systems at a variety of difficult and varied scientific challenges, covering topics as varied as advanced chemistry, computer science, mathematics, classical literature, and ecology.
When the exam launched in January 2025, top scoring AI systems obtained a score of 8%. As of this week, the highest scoring system is Claude Opus 5.5 with 67.7%.
Another example is FrontierMath, a test released in November 2024 that sought to assess how well AI systems could solve hundreds of challenging math problems, curated and evaluated by professional mathematicians. When it launched, the best scoring systems got 2%. Now, they get 94%.
More broadly, it looks like this everywhere. Pick your favorite part of science and go looking for a test and you’ll find this pattern - sudden and steep increases in capability, everywhere you look.
TheoPhysEval
I’ll close my evidence with a story. The latest example comes to us from a paper published just last week, called TheoPhysEval. This is a paper that tests out how well AI systems can solve unpublished problems in theoretical physics. The 50 questions in the paper were compiled by physicists around the world.
Unsurprisingly, AI systems got high scores. But what is surprising is how they got the high scores. Here, I am going to quote from Indian physicist Suvrat Raju, who wrote a post commenting on the results.
“I don’t know of any individual who can solve any significant fraction of our final collection of problems without AI assistance,” he said. “Our initial evaluation suggested that GPT-6-Astra’s score was in the late eighties followed closely by Opus 5.5. But, as we started to investigate the mistakes that the models might have made, the models, especially Astra, insisted that they were right and that it was the expert-supplied answers that were erroneous. When we went back to the experts it turned out, in multiple instances, that the experts found some subtle errors buried deep inside their calculations. As they revised their answers, Astra’s score started to inch up and it finally ended up with 98% (49/50).”
Think about this for a moment. Some of the world’s scientific experts built a test out of problems an AI could never have possibly read the answer to. And when they tested the AI, not only did it score high, but it got a higher score by finding errors in the answers that the humans had worked on!
There is a scene in films, usually two thirds of the way through, when the young protagonist finally proves to their mentor that they have what it takes to go out into the world and succeed independently. It is a narrative trope - when the teacher becomes the student. That is what is happening here.
So, why am I telling you this? Why does it matter that AI systems are getting better at science?
I believe it is because doing true frontier science - being capable of making breakthroughs that extend the frontier of knowledge - implicitly tests for one of the last frontiers of AI capability: creativity. My view is that if AI systems can creatively problem solve to succeed at science, we should expect this to be a sign of their ability to generalize into many domains, including certainly AI research itself, which could have an explosive effect on innovation.
Therefore, if AI systems get better at science, this will strongly drive the growth and shape of the future economy, which means that you can make decisions today to set up your nations and people to benefit from this trend in the future.
Science and growth
Let’s talk about g
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