Introducing Claude for Life Sciences
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Anthropic's Jonah Cool (Head of Life Sciences Partnerships and Deployment) and Eric Kauderer-Abrams (Head of Biology and ...
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中英文字稿 
我们花了三个月的时间，最终动用了很多人，不分昼夜地在实验室工作，才解决了这个问题。我把这个问题摆在Claude面前。我说，嘿，我们该怎么摆脱这个困境呢？结果不到一分钟，Claude就一语道破答案。大家好，我是Jonah Kuhl，负责Anthropic的生命科学合作与部署。大家好，我是Eric Cotter-Abrams，负责Anthropic的生物学和生命科学，我专注于研究和产品开发，我们一起正在努力教Claude成为一名生物学家。
▶ 英文原文 ⏱
It took us three months, ultimately, lots of people working day and night in the lab to fix the problem. I posed this problem to Claude. I said, hey, what should we do to get unstuck? And just in one minute, you know, one response, Claude actually just one-shotted the answer. Hi, I'm Jonah Kuhl. I'm the head of life sciences focused on partnerships and deployment here at Anthropic. Hi, I'm Eric Cotter-Abrams. I'm the head of biology and life sciences here at Anthropic. I'm focused on research and product development, and together we're trying to teach Claude to be a biologist.
好的，Eric，我们来谈谈科学。我对这个话题非常兴奋，也对Anthropic在这个领域的投入感到激动。或许可以先从为什么选择生命科学这个领域开始谈谈，为什么选择Claude，以及Anthropic将给这个已经相当庞大而且发展迅速的生态系统带来什么价值。我认为这是个非常重要的问题。所以，我先从为什么我们专注于生命科学开始说起。这与我们的使命息息相关，而我认为很多人可能并没有意识到这一点。
▶ 英文原文 ⏱
All right, Eric, let's talk science. I'm really excited about this and also excited about the fact that Anthropic and, you know, we're leaning into this space. And, you know, maybe the place to start is just thinking about why the life sciences, why Claude and what Anthropic brings to, you know, what is already a really big ecosystem, but one that's moving really fast. Yeah, I think that's a really important question. So I'll start with why are we focused on the life sciences? This goes right to the heart of our mission. I think it's something that a lot of people may not realize.
当我们谈论人工智能的有益应用及其在我们所开发的前沿AI领域中能带来的各种惊人效果时，Anthropic最期待应用的领域其实是生物学和生命科学。如果你阅读我们的基础资料，或者在这里与同事交流，你会发现这是我们真正专注于实现有益影响的主要领域。对我来说，能够加入这一领域并投入到这些充满活力和激情的应用中，是一件令人非常兴奋的事情。
▶ 英文原文 ⏱
But when we talk about the beneficial use cases of AI and all the amazing things that we can do in the world with the frontier AI that we're developing, actually the number one place that we at Anthropic are excited about applying it is within biology and the life sciences, right? If you read our foundational material and, you know, you talk to people in the hallway here, that's the primary area where we're really focused on delivering the beneficial impact. For me, that's been a super exciting thing to come in and plug into is all of that pent-up energy and excitement to apply everything that we have to the space.
然后，开始更具体地谈论为什么选择Claude，以及我们的Anthropic方法与其他一些方法可能有何不同。我想到的有两点。Joda，你我对此进行了大量讨论。首先，我们感兴趣的是构建工具，以支持个人科学家并提升他们作为科学家的体验，让他们在日常生活和工作中感受到更轻松。因此，我们希望给予人们与软件工程师相同的体验，让他们在整个过程中拥有一个头脑风暴的合作伙伴，可以合作并委派任务。
▶ 英文原文 ⏱
And then starting to get more specific in talking about why Claude and how is our approach as Anthropic, you know, maybe different from some of the other approaches that are out there. I think there are two things that come to mind. You know, Joda, you and I have talked a lot about this. But the first is that we're interested in building tools that empower individual scientists and enhance the experience of being a scientist, going about your life, you know, doing all the work that you're doing, right? So we want to give people the same experience that software engineers have had of, you know, having a brainstorming partner to work with and delegate tasks to throughout the process.
我们希望将这项技术带给实验室中的生物学家以及计算领域的专业人员。因此，我们最初的重点是开发工具，让科学家们工作更高效，同时让科学研究变得更有趣。这样可以减少一些枯燥繁琐的工作，让大家能够更加专注于具有创造力和高价值的方面。这是我们的第一个目标。其次，我们不仅仅关注那些令人兴奋的早期发现问题，如分子设计和蛋白质折叠等，这些都是很多领域的专家都在研究的重要课题。
▶ 英文原文 ⏱
We want to bring that to biologists in the lab and on the computational side. And so our initial focus is really about building tools that make scientists more productive and also make science more fun, right? Take away some of the grunt work that, you know, everyone would rather get out of and allow you to focus more on the creative high leverage side. So that's the first part. And then the second one is we're really focused not just on the really exciting early stage discovery problems, right? Molecule design and protein folding and these, you know, incredibly impactful problems that many people in the field have focused on.
我们希望涵盖整个过程，从早期的发现阶段一直到开发和转化。因此，对我们来说，这意味着要分解这个领域中存在的各种任务。从起草和审核方案、对其进行调试，到进行生物信息学分析、将结果写成幻灯片和论文等等。这些都是非常重要的任务。我们采用整体的视角来解决所有这些任务。
▶ 英文原文 ⏱
But we want to address the whole spectrum from early stage discovery all the way through development and translation. And so for us, that means, you know, breaking it down into the whole world of different tasks that exist in the space. Everything from, you know, drafting and reviewing protocols and debugging them to performing bioinformatics analyses and writing up your results in slides and papers and that sort of a thing, right? There's a whole world of tasks out there that are important. And we're taking a holistic view in addressing all of them.
是的，我认为现在是一个非常有趣的时刻，值得思考科学，或更广泛地说，思考人工智能。人们往往会倾向于问："人工智能能为我解决什么问题？" 但是，就像你刚才很好地描述的那样，我们可能还需要从一个稍微不同的角度考虑这个问题，例如，如何改变我们进行科学研究的方式。这最终会影响到我们解决结构、分子、组织、成像等问题，并开始深入思考这一领域的世界。
▶ 英文原文 ⏱
Yeah, I think it's a really interesting time to think about science and maybe AI more generally. And, you know, there's this inclination towards, you know, what problem will AI solve for me? But, you know, the way that I think we're thinking about it, the way that you just really nicely described in the way that in Machines of Love and Grace is maybe also thinking through that, like, slightly orthogonal point, which is, like, how do we change how we do science? And that will then impact, you know, solving, you know, structures and molecules and tissues and imaging and starting to, like, think through that world.
所以，你知道，考虑到这些，也许可以过渡到下一个话题。我们都见识过Claude的强大功能，我觉得我们都在使用Claude进行科学研究时感受到了乐趣和喜悦。目前，它的能力已经如此出色。而现在，在你领导的研究小组中，我们也开始思考如何进一步提升这些能力。也许我们可以聊聊当前的能力和生态系统，甚至通过MCPs（计算机多学科项目）以及生命科学等扩展的联系来创造一个基础模型。然后谈谈你对如何扩展这些能力的看法，甚至进一步推进这些能力，并且如何让这些设想开始成型。
▶ 英文原文 ⏱
So, you know, with that in mind, maybe then to transition. So, you know, we've seen the power of Claude. I think both of us have experienced this and, like, the delight and the joy in doing science with Claude and its current capabilities. But then also now starting to think in the research group that you're leading and how we advance those capabilities. And so maybe for a minute, we can just chat a little bit about how kind of current capabilities and ecosystems and maybe even, like, extended contacts through MCPs and life sciences start to create this base case. And then your thoughts on how we extend that and even, like, push it further and, you know, what that starts to look like and take shape.
当然，没错。我们之前已经讨论过很多次了。我认为，在这个领域，我们需要采取循序渐进的方法。理解生物学并让人工智能在科学中发挥作用，与人工智能在其他领域的应用是不同的。也许可以用一个旧的跑步比喻来说明：一开始加速，中段加速，最后全力冲刺。几乎没有慢步，只是在全速冲刺，然后冲得更快，比坐火箭还快。这就是我们真正想要达到的目标。
▶ 英文原文 ⏱
Yeah, totally. So, you know, we've talked about this a lot. And I think that, you know, it's important to crawl, walk, run in this space, right? It's – there's a lot of things about doing biology and having AI, you know, be useful in science that are different from having AI be useful. It is the AI space. So maybe you like to use an old running analogy. You know, you start fast, you pick it up in the middle, and you sprint home. So very little crawling, but just, you know, sprinting and then sprinting faster. Sprinting, sprinting faster than flying in a rocket ship. That's really right, what we're going for here.
但是，你知道，最基本的层面是，我们需要Claude能够熟练使用科学家每天使用的所有工具，对吧？因此，我们正在整合一个由重要工具和合作伙伴组成的完整生态系统。比如说，BenchLink在实验管理和实验室笔记本方面非常重要，还有TEDx Genomics的CellRanger，是分析单细胞实验的重要平台。再比如，我们使用PubMed查询文献。这三个例子只是这个更大生态系统中非常重要的合作伙伴。
▶ 英文原文 ⏱
But, you know, the very base level is we need Claude to be conversant with all of the tools that scientists are using every day, right? And so there's a whole ecosystem of important tools and partners out there that we are integrating with, right? So we talk about BenchLink on the, you know, experiment administration, lab notebook side of things. TEDx Genomics with CellRanger, right? Incredibly important platform for analyzing single-cell experiments. And then PubMed, for example, for being able to query the literature, right? And so these are just three incredibly important partners in a much larger ecosystem.
我们首先需要确保Claude能够与科学家在日常工作中使用的所有主要信息来源进行沟通。这是基础。然后，我们希望提高Claude的能力，使其成为一名超凡的研究助手，能够在项目的各个阶段帮助科学家。从早期的假设生成阶段，当你在进行创造性的工作、审查文献以及头脑风暴时，到实验执行阶段，包括起草实验方案、在实验室中解决问题，甚至实际进行实验，以及后续的数据和计算分析阶段。
▶ 英文原文 ⏱
And so that base level is we need to make sure that Claude can talk to all the major sources that scientists are using throughout, you know, their daily work. And then I think the next level is we want to bring Claude to performing at the level of a superhuman research assistant that can assist you as a scientist throughout all stages of your project, right? From the early stage hypothesis generation when things are more creative and you're reviewing literature and you're brainstorming to the experiment execution phase where you're drafting protocols and you're debugging things in the lab and even actually running those experiments in the lab to the computational and data analysis side of things, right?
当你在运行生物信息学脚本时，你还在进行机器学习或一些统计分析，然后你把结果展示给同事或自己，对吗？在这个过程中，我们已经将任务分解到所有这些领域中，并且我们正在研究如何评估我们的模型在这些任务上的表现，以及如何快速提升各个领域的性能。我们正在大力投入来实现这些目标。
▶ 英文原文 ⏱
When you're running your bioinformatic scripts, you're doing machine learning on top of that or some statistics and you're presenting the results to colleagues or for yourself, right? And so here this is where we've broken down tasks into all of those areas, right? And we're figuring out, all right, how do we evaluate how well our models are doing those tasks and how do we, you know, rapidly improve performance in all of those areas? So we're making a big investment in doing that right now.
我认为有必要指出，我们不是在泛泛而谈。生命科学并不是一个单一的整体，而是由许多不同的子领域组成的。在我们看来，生命科学有一个核心部分，它包括许多不同领域中共享的重要任务。然后，在这个核心之内，还有一些特别重要的子领域。我们希望能全面覆盖这些领域，但我们现在特别专注于从这个在整个过程中都有用的核心任务开始。
▶ 英文原文 ⏱
And I think it's also important to say that, you know, we're not just doing this generically, right? Like, in some ways, you can't speak of life sciences as one monolithic thing, right? There's all these different subfields within it. And we have a particular sequencing in mind where we like to think of it as being consisting of this core of, you know, important tasks that are shared throughout many different fields. And then within that, there are different subdomains that are really important, right? And we want to address all of it. But we're really focused on starting with that core that's going to be useful throughout the whole journey.
你知道吗，我对我们目前的合作伙伴以及那些早期参与打下基础的人感到非常兴奋，特别是与MCPs相关的人。你提到了10x Genomics，你还提到了PubMed，还有像Benchling这样的团队，然后是Sage Bionetworks和BioRender。回到刚才所说的，这不仅仅是在解决一个问题。这些合作伙伴中，你可以看到文献资料、仪器设备、分析流程，还有如BioRender那样提供完美图片或网络图的锦上添花。这一切都展示了综合多方资源的潜力，希望能付诸实践。
▶ 英文原文 ⏱
And, you know, one thing that I'm really ecstatic about with our current partners and, you know, folks that are involved early on here to build this foundation, especially with MCPs, is that you mentioned 10x genomics. You mentioned PubMed, you mentioned groups like Benchling, and then, you know, Sage Bionetworks, BioRender, you know, kind of going back to that last point, it really demonstrates and hopefully puts to action the fact that it's not just solving a problem. But, you know, in that group, you've got the literature, you've got instrumentation, you've got analytical workflows, you've got the cherry on top with that, like, perfect image or, you know, network diagram and BioRender.
我预计在接下来的几周和几个月中，这整个生态系统将会呈指数级增长。这将为越来越多的科学家提供更强大的能力。我觉得这真是非常酷和令人兴奋的观点。因为这正是我们许多人在软件领域的经历。对我来说，我一直在软件世界和生物世界之间徘徊。事情从软件领域开始，你给Claude一些小任务，然后随着时间的推移，这些任务演变得更长，Claude变得更加自主，能够更无缝地整合进各种工具中。
▶ 英文原文 ⏱
And I expect that over the weeks, months to come, like, that whole ecosystem is just going to grow exponentially. And with that, like, the power for more and more scientists. And I think that's just, like, incredibly cool and exciting. Yeah, I think that's a great point. Because that's the experience that, you know, a lot of us have had on the software side, right? For me, I've always been, on the one hand, a part of the software world, the other hand, a part of this bio world. And, you know, things started on the software side where you'd give Claude, you know, these little snippets of tasks, right? And over time, those tasks become longer horizon. Claude becomes more autonomous, you know, can more seamlessly integrate to the different tools there.
我认为我们正处于生命科学的一个起飞点。现在，通过引入各种连接，我们能够开启下一个阶段。在这个阶段，你不再需要只是让Claude去执行一个分析，然后做一些工作，再回来制作一个生物图，再请Claude修改。实际上，我们可以把原本需要人类科学家花费几个小时完成的工作交给Claude。我认为这一转变是该领域中一个非常激动人心的时刻，从一种实用工具转变为一个头脑风暴的伙伴。就像我听说的那样，它已深深嵌入到整个过程，成为一个真正的合作者。
▶ 英文原文 ⏱
And I think we're right at that takeoff point in the life sciences where we're just now, with all of these connections that we're introducing, able to unlock that next stage where, you know, you don't have to just ask Claude to go perform an analysis. And then you do some work, and then you come back, and you make a bio render fair, and you ask Claude to revise it, right? We could actually give Claude a whole, you know, meaningful chunk of the work that would take a human scientist a couple hours to do. And I think that transition is the really exciting point in a field where it goes from being, you know, a useful kind of utility to actually a brainstorming partner, right? Which is what I've heard. It's just kind of, like, embedded in the process. A collaborator, yeah.
我们最近发布了Sonnet 4.5，这是一个非常令人兴奋、功能强大的模型。我认为，我们已经看到了一些事情，当然也很期待听到您在研究方面的看法，就是看到这个模型在不同科学领域的表现。因此，您可能会在这些模型的演变和能力，以及我们在与科学家相关的不同任务中的早期评估或基准测试中发现了一些什么？关于Sonnet 4.5，我觉得有两点让我非常兴奋，并且极大地增强了我的工作。第一点是，这是我们第一个经过广泛科学训练的模型。
▶ 英文原文 ⏱
So we recently released Sonnet 4.5, a really exciting, super powerful model. And I think one of the things that we've seen, and, you know, eager to hear your perspective on the research side, is just, like, seeing how that model performs in the context of different areas of science. And so, you know, what have you seen maybe in the, like, the evolution and the power of those models and maybe some of the early evals or benchmarks that we've been seeing in different tasks that are relevant to scientists? So I think there's two things about Sonnet 4.5 that I'm really excited about that have enhanced my own work by a great deal. The first is that it's our first model that's undergone extensive scientific training.
Sonnet 4.5在多个科学领域中都表现出色。一个令人兴奋的方面是，它的许多能力可以被广泛应用。Sonnet 4.5在数学方面的提升，实际上也对生物学尤其是计算层面产生了积极影响。我们很高兴看到这是我们第一个在科学上真正能力超群的模型。同时，在训练过程中采用了一些创新方法，使得这些能力成为可能，我们将在未来不断借鉴并加速这些方法。另一个值得一提的功能是，它能够处理长时间的任务，这些任务包括连续调用多种工具。
▶ 英文原文 ⏱
So Sonnet 4.5, you know, is skilled in many different domains of science. And I think one of the exciting things is that there's a lot there that generalizes, right? And so Sonnet 4.5 being better at math, you know, has some effect of uplifting different capabilities in bio, especially in the computational side. And so I think it's just really exciting that it's our first, you know, scientifically, you know, really, really capable model. Yeah. And, you know, there were some new things on the training side that went into making that possible that we're just leaning into and accelerating with all future models here. And the second thing is its ability to do long horizon tasks, right, consisting of long strings of different tool calls.
所以，对于那些从事过这种长时间的生物信息学流程的人来说，这是绝对关键的能力。我们在 Sonnet 4.5 中看到这些能力有了显著的提升，它独特地能够开始执行这些非常长的生物信息学工作流程。我认为，在分析流程中，思考如何应用到 Claude 的不同界面也很重要。很多人提到 Claude 时会想到聊天界面，但实际上，对于许多科学家来说，尤其是一些可能意识到或未意识到这一点的科学家来说，像 Claude code 这样的自主编码工具的强大功能在数据分析、整合以及各种知识类型的推理中变得非常有趣。
▶ 英文原文 ⏱
So this is something that, you know, for anyone that's done these sorts of long bioinformatics pipelines and things like that is absolutely critical. And we saw a major jump up in those capabilities with Sonnet 4.5, which, you know, makes it, you know, uniquely able to start to do these, like, really long bioinformatics workflows. Yeah, I think in the analysis workflows and also thinking about how it applies to the different surfaces of Claude. So, you know, a lot of people think about Claude and they think, you know, the chat interface. But, of course, I think for many scientists, maybe some that do, maybe some that don't realize this, the power of agentic coding tools like Claude code or other places where that longer context and all that power becomes really interesting for data analysis, for integration, for kind of, like, reasoning over different types of knowledge.
这真是一个令人难以置信的起点，对吧？我们可以从这里开始构建。是的，确实是这样。我知道这是你我一直以来最兴奋的事情之一，Claude代码如今在生物学上有着惊人的实用性。而大多数人没有意识到这一点，对吧？它被称为Claude代码，而不是Claude生物学。然而，在它的核心，有一个非常强大的通用智能体，特别是我和我们在社区中交流的许多人，已经开始在生物信息学中使用它，甚至用于撰写论文草稿、进行文献综述和组织项目。因此，我认为我们肯定会更投入地推动这方面的发展。
▶ 英文原文 ⏱
And it's an incredible, like, starting point, right, where we can start to build. Yeah, it really is. And I know this is one of the things that you and I have been the most excited about, that Claude code is amazingly useful, as it is today, in biology. And most people don't realize that, right? It's called Claude code. It's not called Claude biology, right? But, you know, underneath the hood there, there's a really powerful general purpose agent that I, in particular, you know, many people that we've talked to throughout the community have started to use in bioinformatics, even in things like drafting papers, right, and in performing literature reviews and organizing your projects, right? And so, I think that's definitely something that we're going to be putting a lot more energy into.
是的，是的，我的意思是，你知道，会有那些时刻。作为一名技术专家，同时也是一个热爱开发技术并将其应用于生物学的人，我们都对此有着共同的兴趣。你知道吗，会有那些时刻，你看到或体验某些技术，然后你真正感受到它们的力量。我仍然记得那个让我振奋的时刻，就像是第一次使用Claude代码，实现了一些超出我技术能力的任务变得可行并且易于管理，或者是那些仅仅是耗时又繁琐的工作流程运行和执行也变得轻而易举。我的意思是，这些工具如此强大地交到科学家手中，确实如此。
▶ 英文原文 ⏱
Yeah. Yeah. I mean, you know, there's those moments. And as a technologist and someone that loves to develop technologies and apply them to biology, which is an affinity that I know we both share, you know, there are those moments where you see technologies or kind of, like, experience technologies and you just, like, really feel them. And I still have that moment of, like, uplift, you know, remembering the first time, like, playing with Claude code and making tasks that are either kind of beyond my technical capabilities, tractable and manageable, or the tasks of just, you know, like, workflow running and execution that are just time-intensive and cumbersome, trivial, right? I mean, it just, like, puts those tools in scientists' hands in a way that is incredibly powerful. It really is.
你提到那些让你从内心深处感受到新技术能力的时刻，这让我想起了一个真正让我大开眼界的瞬间。这其实可以追溯到 Sonnet 3, 5 版本的日子，当时我意识到这些大型语言模型和前沿模型对我们生命科学工作的巨大影响。对我来说，那一刻是这样的：当时我正经营着一家我创办的生物技术公司，我脑海中闪现了一个念头：如果我在五年前创办公司时就能使用 Claude，我们将会节省多少时间？在解决一些非常困难的研发问题时，它又能帮我们避免多少挫折和麻烦呢？
▶ 英文原文 ⏱
And, you know, you mentioning those moments where you just viscerally feel, you know, the new capabilities that are out there, that reminds me of that moment for me that really woke me up for the first time. And this was actually back in the Sonnet 3, 5 days, that, wow, these LLMs and these frontier models are really relevant for what we're doing in the life sciences. And so, for me, that moment was, at the time, I was running a biotech company that I had founded, and I had this idea that I wanted to try to see, hey, if I had had access to Claude when I was starting this company five years ago, how much time would it have saved us? And how much heartache in trying to navigate some of these really difficult R&D problems we were trying to solve would it have saved us?
我永远忘不了这件事，因为当我们创办这家公司时，我们遇到的第一个巨大的战术障碍就是这样。当时我们正在开发一种检测手段，试图检测COVID病毒，但遇到了问题。我们受到了样本基质的抑制，怎么也找不出问题所在。最后，我们花了三个月的时间，很多人日夜在实验室工作才解决了这个问题。我向Claude提出了这个问题，我说：“我们正在尝试开发这种检测手段，但样本在抑制反应，我们该如何摆脱这种困境？”
▶ 英文原文 ⏱
And I'll never forget this, because the very first huge tactical roadblock that we ran into when we founded this company, there was a problem we were developing an assay, you know, trying to detect, in this case, COVID. And it wasn't working. We were getting inhibited by the sample matrix, and we couldn't figure it out, right? And it took us three months, ultimately, and, you know, lots of people working day and night in the lab to fix the problem. And I posed this problem to Claude. I said, hey, we're trying to develop this assay, and we're seeing that the sample is inhibiting things, and what should we do to get unstuck?
在短短一分钟内，Claude就给出了一个回复，实际上一下子就找到了答案，并且建议说，我认为你应该在混合物中添加这么多的这种化学品。这真是一个让人大开眼界的时刻，对吧？通过与Claude交流，你仿佛在与一个浓缩版的科学知识总和对话。当时，这个知识库还不完美，但正在快速进步中。总是存在着这种紧张感，我想科学家们追求的是完美，对吧？这是一种我们都努力追求的东西，希望能够达到那种准确性。
▶ 英文原文 ⏱
And just in one minute, you know, one response, Claude actually just one-shotted the answer and said, hey, I think you should add this much of this chemical, you know, into the mix. And, you know, that was a really eye-opening moment, right, that here, in conversing with Claude, you know, you're kind of talking to a distilled version of the totality of, you know, scientific knowledge, right? And at the time, it was imperfect, but it's rapidly getting better. There's always this tension, and I think scientists want perfection, right? It's something that we all kind of, like, strive for and want that specificity.
对于那些阻碍科学进展的工作，比如协议优化，一个不完美但有帮助的答案通常是我们会去咨询最值得信赖的同事，他们可能会说：“嗯，我不太确定，但这看起来很熟悉，对吧？我在某个时候见过这个问题。”这些有点像智慧教授，或者是在走廊尽头的超级聪明的学生。我们不是在寻求完美，而是想找到解决困境的方法，想有所帮助，想让你继续前进，走向新发现，这不正是我们大家都在追求的吗？
▶ 英文原文 ⏱
But for a lot of the work that holds science back, protocol optimization, you know, an imperfect but helpful answer is the sort of thing that we go to, you know, the most trusted colleagues where they might say, like, eh, I don't know if – but, like, this looks familiar, right? Like, I've seen this problem at some point. Those kind of, like, sage professors, they're the, like, super sharp student, you know, down the hall. And, again, it's not looking for perfection, but it's looking to get unstuck. It's looking to be helpful. It's looking to just, like, keep you moving and, like, towards discovery, which is what we're all looking for, right?
是的，完全没错。而且我认为，另一个很早就让我注意到的领域是在法规程序中，这在后期的翻译阶段也是相关的。我花了很多时间撰写法规提交文件，并且与FDA一起经历这些流程。你知道吗，Claude在这方面真的很有能力。我认为在行业和FDA两方面都有巨大的机会，我们可以利用这些工具来加快双方的流程，并促成全行业一致的标准。我对这一点感到非常兴奋，也知道整个行业的人也对这方面充满热情。
▶ 英文原文 ⏱
Yeah, totally. Totally. And I think the other area that really jumped out for me early on was, you know, this is relevant later in the translation phase, is in the regulatory process, right? So I've spent a lot of time writing regulatory submissions, going through those processes with FDA. And, you know, Claude is really capable there. And I think there's a huge opportunity, both on the industry side and on the FDA side, to recognize that we have these tools that can, you know, speed up the process on both sides and facilitate consistent standards across the board. And I'm really excited about pursuing that. And I know people, you know, across this whole industry are as well.
好的，那让我们停留在这个话题上一会儿。你知道，在生物学和人工智能领域，甚至如果你退一步考虑，单从工程和技术的角度来看，生命科学和生物学常常被视为人们非常感兴趣的一个领域。大家对于生物学的兴奋点在于，它似乎离能够立即被编程化只有一步之遥。我们大概都同意其中的一些观点和直觉。但在很多情况下，其实是一些人可能更热衷于生物学这个概念，而不是真正了解生命科学涉及的内容和相关的监管框架。
▶ 英文原文 ⏱
Okay. So let's stick on this for a minute. You know, within biology, within AI, maybe even if you take a step back from AI and think about just, like, engineering and technology, you know, the life sciences, biology is this, you know, frequent substrate where people get really excited about the idea of biology or how it's just, like, one step away from being, like, immediately programmable. Some of those ideas and intuitions I think we probably agree with. But I think in many cases, you know, it's folks that are maybe more in love with the idea of biology as opposed to, like, really know what the life sciences, what regulatory frameworks look like.
你知道吗，让我们再多谈谈，关于你的经历，我们作为科学家的集体经历，以及如何将这些细致的知识带入我们的合作伙伴关系和研究工作中。回顾过去，这些时刻对你来说是怎样的，又如何影响到现在的优先事项或方法。我认为这是一个非常重要而且相当有趣的话题。在某种程度上，这就像是计算机科学家、物理学家、数学家进入生物学领域的经典情节，他们带着浪漫的想法，然后在实验室的第一年过后，可能会有点震惊，甚至有些幻想破灭，意识到有些事情并不像想象中那么容易实现。
▶ 英文原文 ⏱
You know, let's talk a little bit more about, you know, your experience, our collective experience as scientists and kind of, like, bringing some of that detailed knowledge to our partnerships, to our research efforts, and, you know, what those moments have looked like for you in the past and how they're maybe, like, reading on to priorities or approaches. Yeah, I think this is a really important and also pretty fun topic to talk about, right? In some ways, it's one of the oldest tropes in the space of the computer scientist, the physicist, the mathematician that kind of waltz into biology and have all these romantic notions and then, you know, spend their first year in the lab and come out kind of shell-shocked and, you know, in some ways disillusioned of all the things that are possible, right?
我认为，你知道，你我都来自相似的背景，我们在这里的工作方式是基于我们对实验室生活的理解，并且我们想解决这一领域的真正瓶颈，对吧？我可以说这也是我的背景。我更多地是从计算机和数学的角度出发，然后在实验室工作中逐渐积累了生物方面的知识。我并不感到失望。我真正相信我们有机会极大地提升生物学家的能力，让他们进行令人难以置信、影响深远的研究，并且凭借我们现在拥有的工具，这一切终于有可能实现了。
▶ 英文原文 ⏱
I think that, you know, where you and I both are coming from and the way that we're doing things here is we know what life in the lab is like and we want to solve the real problems that are the bottlenecks for this field, right? I will say that that's my own background. I'm coming more at the computer side side and the math side of things and have picked up bio, you know, over the years in being in the lab. And I'm not disillusioned. I really believe that we have the opportunity to massively uplift the capabilities of biologists in doing incredible, impactful research and that with the tools that we have now, you know, we're finally at that moment where all these things are possible.
所以，我依然保持着我最初涉足这项工作时的乐观态度。我觉得，在实验室里获得的所有经验真的帮助我理清了一些问题，指出了那些确实存在的问题，这些问题并不简单，需要大量艰苦的工作去解决。不过，我认为我们现在已经做好了应对这些问题的准备。所以，我还是很乐观的。不过，我很想听听你的看法。
▶ 英文原文 ⏱
So I remain the same optimism that I had when I first got into this. I think, you know, all the experience in the lab has been really clarifying to help point out, okay, there are real problems here that are not pretty and that require, you know, lots of grindy work to get in there and disentangle. But I think we're now set up to make a dent in that. So, yeah. But I'd love to hear what you think.
是的，我完全同意。我也很乐观。我认为很多人并不总能理解科学有多么困难，以及坚持的重要性。我觉得这一点同样适用于临床研究。在科学研究中，每个步骤都需要整合大量的知识，无论是优化实验方案还是数据分析，生物学的复杂性让这些过程变得格外艰难。要一个人掌握所有这些专业知识几乎不可能，甚至一个团队或机构都未必能做到。
▶ 英文原文 ⏱
Yeah, I mean, I totally agree, right? I mean, I share that optimism. I do think that there are many people that don't always understand, like, how difficult science is, but also how important just persistence and the fact that, you know, research and I think this probably applies, you know, down the clinical pipeline too. You know, because it's because it's so difficult, because there's so much knowledge that needs to be incorporated in every, like, step in debugging and the complexity of biology, whether it's that protocol optimization or data analysis, it's really hard to hold all that expertise definitely in one person, probably not even in one group, and infrequently in one institution.
这带来的结果是——我认为我们在CLOD技术上提供了非常强大的支持，同时作为一个研究助手和合作伙伴——它开始带来更多的灵活性。对于那些可能没有计算机科学背景的人来说，它降低了进行计算分析的门槛。对于那些没有一生都在从事克隆和分子生物学研究的人来说，它带来了某些分子生物学和优化的技能。并且，它也帮助使发现变得可以在不同领域之间转移。
▶ 英文原文 ⏱
And the result of that, and I think where, again, we provide a really powerful technology in CLOD and a, you know, research assistant collaborator, is it starts to, like, bring more of that fluidity, right? It lowers the bar for computational analysis for folks that may not have that computer science background. It brings some molecular biology and optimization skills for folks that haven't spent their whole life, you know, cloning and doing molecular biology. And then it also just, like, helps make discoveries, you know, transferable across fields, right?
我的意思是，我并没有接受过神经科学家的训练。我过去喜欢参加神经科学的讲座，但参加完后总是要回来问一些很幼稚的问题。不过，当我第一次看到神经科学中发现的光遗传学时，我发现它花了很长时间才应用到细胞生物学和其他领域。我认为CLOD的魅力在于，它作为一名生命科学家，开始解决一些生物学中的核心问题，同时也在逐渐打破科学中一些令人生畏的壁垒，使科学变得更加流动和易于理解。
▶ 英文原文 ⏱
I mean, I was not trained as a neuroscientist. I used to love to go to neuroscience lectures, but would then have to, like, come back and either, like, ask a whole bunch of naive questions. But, you know, seeing optogenetics for the first time, you know, discovered in neuroscience took way too long to get out to cell biology, to other domains. And I think the power of CLOD and CLOD as a life scientist is it starts to, like, address some of those core problems in biology, but also just starts to create that fluidity and start to break down walls and some of the parts that makes science hard.
我完全同意。当我们谈论展望和研究路线图时，我还想提到，我们之前非常注重那些基础任务，就像吃肉和蔬菜一样，这些都是些非常实用但有点浅层的任务。同时，我也想指出，我们发现领域内越来越多的人开始关注生物基础模型。这些模型在处理生物学信息方面表现出类似天才的能力，比如在DNA和蛋白质序列、数据的多模态表现等方面的应用。
▶ 英文原文 ⏱
I totally agree. And the other thing I want to mention when we're talking about our outlook and our research roadmap and things like that is, you know, we focused a lot on the meat and potatoes and eating our vegetables of, you know, all of these practical tasks, right, that are really exciting, but more sort of surface level. I also want to call out that we're seeing an increasing trend in, you know, the field focusing on these biofoundation models, these models that have savant-like capabilities on biological modalities, right, DNA sequences and protein sequences and being multimodal and expression data and all sorts of things.
一个非常有趣的趋势是，随着时间的推移，我们看到越来越多的论文发表，它们证明了以前看似需要特殊生物模型才能实现的东西，也许实际上不需要那些特殊模型。也许实际上通过使用非常大型的前沿模型，比如Claude，并在适当的训练下，我们可以开始开发那些能力。我认为我们整个领域都在刚刚开始探索这个方向。但我觉得这是一个非常令人兴奋的趋势，我们会非常积极地去追求这个方向。因为我认为拥有这些特别出色的生物模式功能对于具体的生物基础模型是非常有力的。但为了让这些能力真正对人们可用，就需要能够通过语言进行交互。因此，我想特别提到这是一个非常有趣的特征。
▶ 英文原文 ⏱
And a trend that's really interesting to watch is seeing, you know, increasing number of papers come out over time that are demonstrating that these things that previously looked like you needed these specialized biomodels for, maybe you don't. And maybe actually with, you know, really large frontier scale models like Claude, with the right type of training, we can start to develop those capabilities. And so I think we're all as a field at the beginning of just sort of working through that. But I think it's a really, really exciting trend to follow and that we'll be pursuing pretty aggressively, right? Because I think having these savant-like capabilities in these biomodalities is really powerful for these specific biofoundation models. But to really make that accessible to people, you need to be able to interface it with language, right? And so I wanted to call that out as one interesting feature.
是的，我的意思是，这是一个很好的观点，你知道，随着这个领域的发展，我指的不仅是人工智能领域，还有生命科学的许多领域。我认为我们已经看到了一大批令人兴奋的合作伙伴，包括那些在生物技术领域中运用人工智能的初创公司，它们正在使用这些工具，还有大型制药公司的参与。不同部分的结合方式，生物基础模型、通用智能模型、特定数据集等，这一切都将非常吸引人。我认为这是一个激动人心的时刻。或许这也涉及到合作的要点，就是如何将这些不同的部分结合起来，对合作的看法，一些初期的学习经验或合作机会，或者建立这个生态系统的理念。
▶ 英文原文 ⏱
Yeah, I mean, it's a great point that, you know, as the field progresses here, and by field here, I mean both, you know, the field of AI, but also like many domains of life sciences. And I think we're already seeing a whole bunch of really exciting, you know, partners that are the AI-native startups in the biotech space that are kind of taking some of these tools, as well as large pharma partners. And the way that the different pieces come together, right? So biofoundation models, general intelligence models, specific data sets, you know, it's going to be fascinating, right? And I think a really exciting time. And maybe this also gets to the point of partnership, right? So starting to take those different pieces and like bringing them together and how we think about partnerships, maybe some of the early learnings or opportunities or partnerships that have been front of mind for you or kind of a philosophy of like building this ecosystem.
好的，我是这样理解的：我们明确了我们的北极星目标，即实现Dario在《机器的仁慈》中描述的那个神奇世界。在这个世界中，生命科学领域的研发能够至少快十倍。我们希望尽快实现这一目标。在这个框架下，我认为建立合作伙伴关系非常重要。我们需要确保所有关键要素都具备。其中一些要素我们将通过自身努力实现，比如模型训练和部分产品开发。然而，其他要素的实现则需要我们找到合适的合作伙伴，并尽最大努力给予支持。
▶ 英文原文 ⏱
Yeah, so the way that I think about it is we know what our North Star is. We want to enable the amazing world that Dario writes about in Machines of Loving Grace in which, you know, R&D throughout the life sciences is accelerated by at least an order of magnitude. We want to make that happen as soon as possible. And within that framing, I think about partnerships is we need to make sure that all the right pieces exist, right? Some of those pieces we're going to do ourselves, right? A lot on the model training side, some on the product side as well. But other pieces, you know, make sense for us to just find the right partners and make sure that we're supporting as much as we can.
当我考虑不同类型的合作伙伴时，生态系统合作伙伴显得尤为重要。例如，Benchling 就是对我们非常重要的合作伙伴之一。我认为，大多数从事生物科学的研究人员都在使用 Benchling 来管理和运行他们的实验及数据。这对我们来说是一个非常重要的合作方向。我们正在与他们合作，并将很快分享一些令人兴奋的进展。这是其中一种类型的合作伙伴。 另一种类型的合作伙伴是那些利用我们所构建的技术来进行科学研究的合作伙伴，这些研究以前无法实现。无论是单位时间内完成更多的科学研究，还是其它方面，都是我们想与之合作的伙伴类型。
▶ 英文原文 ⏱
And so when I think about the different types of partners, there's really important ecosystem partners, right? Like I would call out Benchling is one of those for us where I think they have, you know, the majority of working, you know, bioscientists are using Benchling is how they engage every day with kind of managing and running their experiments and their data. And so that's really, you know, important one for us to lean into. And I think there's a lot of exciting things that we'll be able to share soon that we're working on together. So that's one type of a partner. Another type is a partner that we want to work with in which they're using what we're building to actually do science, you know, in a way that wasn't possible before, right? Whether it's doing more science per unit time, right?
得到比以往更有影响力的结果，或者实现以前不可能的发现，对吧？因此，有一些合作伙伴关系让我们非常兴奋。我认为值得一提的是与ARC研究所的合作。我知道你对这个也很有想法，所以我们很想听听你的看法。是的，我认为，我们都对Anthropic有一种特别的亲和力，因为其模型具有独特的深度思考功能。我不认为这么多科学家自然而然地倾向于使用Claude是偶然的。
▶ 英文原文 ⏱
Getting more impactful results per unit time than they could otherwise, or making a type of discovery that wasn't possible before, right? And so there, you know, there's a few partnerships that we're pretty excited about. I think one that's worth mentioning is with the ARC Institute. Yeah, I know that you're thinking a lot about this as well. So we'd love to hear your thoughts. Yeah, I mean, I think the, you know, the affinity that we both had towards anthropic because of the unique features of the models, you know, this like deep thinking. I don't think it's an accident, actually, that so many scientists have gravitated towards using Claude just, you know, naturally.
但同时，达里奥的愿景是我们非常认同的愿景，也就是我们的目标是加速发展，对吧？本来需要100年才能完成的科学，现在在10年内就有可能实现。这很大胆，也很有雄心。但我认为，当你越多地思考，以及思考是什么阻碍了科学的发展时，你会发现这是可以实现的。因此，我同意，在生命科学和生物学领域，另一个独特之处是这个生态系统极其连贯和流动。今天在实验室完成论文的学生，明天就可能成为一家以人工智能为核心的初创公司的创始人，然后，这家公司可能被像礼来这样先进的人工智能制药公司收购、合作或推动主要研发项目的发展。
▶ 英文原文 ⏱
But then also Dario's vision, and I think the vision that we very much believe in, which is, you know, our goal is to accelerate, right? It's 100 years of science that is possible in 10. That's bold. It's ambitious. But also, I think the more you think about it, and the more you think about what holds science back, it's achievable. And so I agree, you know, within the life sciences and biology, I think the other thing that's unique is that it's an ecosystem that is incredibly continuous and fluid, right? The student that is in a lab and finishing their thesis one day is the founder of an AI-native startup, you know, the next, that is then, like, acquired or working with or advancing, you know, major pipelines at, you know, AI-forward pharma companies like Lilly.
而且，你知道，在思考整个生态系统中的合作关系时，那种灵活性是非常有利的运用。这是科学的全部内容以及实现这些目标的方式。我非常兴奋的一个方面是我们还没有提到的——我们的“科学AI”项目。这个项目旨在将工具和Claude技术交到那些拥有大胆想法或重大项目的科学家手中，他们相信Claude可以在解决问题上发挥作用。我认为这是一种推动早期发现研究的绝佳方式，也是我们与这些合作伙伴密切合作、相互学习的好方法。
▶ 英文原文 ⏱
And, you know, that fluidity and thinking about kind of that entire partnership in that ecosystem, I think is that that's the beneficial deployment. It's all of science and achieving that. The thing that I'm really excited about and maybe one feature you didn't touch on is our AI for Science program. And this is really looking to put tools and Claude into the hands of scientists that have a bold idea or a big project, and they think that Claude can, you know, be useful to solving that. And I think it's a great way to, you know, power early-stage discovery research. It's a great way for us to kind of lean into those partners and work with them closely and learn from them.
并且，要不断地、你知道，逐渐扩大视野，并在这些早期阶段理解，哪些东西真的运作得很好？同时，说实话，同样重要的是，哪些方面没有做好？我们都相信这种力量，但也承认目前的不完善。因此，这给了我们一个机会，与科学家合作，加速他们的研究，根据他们的成功、发现、加速、时间来判断成功，并开始看到我们在哪些方面做得很好，或许也发现一些需要大幅改进的领域。
▶ 英文原文 ⏱
And, like, start to just, you know, keep drawing the aperture open and understanding, like, in these early days, you know, what is working really well? And, you know, frankly, equally important, like, what isn't working well? And, you know, I think we both believe in the power but also believe in the current imperfection. And so that opportunity to, you know, work with scientists, accelerate their research, judge success based on, you know, what their success is, their discovery, their acceleration, their time, and start to see where we're doing pretty well and maybe some areas where we just need to be doing a lot better.
是的，我对此也感到非常兴奋。我认为这是一个非常重要的观点，对吧？在这次对话中，我们一直强调要把问题分解成各个部分，然后独立解决。但最重要的部分是我们把所有这些部分重新组合起来的时候。科学家们实际上正在使用这些工具，了解它们的进展和我们具体在做什么。因此，我认为科学领域的人工智能项目对于我们来说至关重要，因为它能让我们获得反馈，并确保与每天在实验室里使用这些工具的人保持互动。因此，我对此感到非常兴奋。
▶ 英文原文 ⏱
Yeah. I'm really excited about that, too. And I think that's such an important point, right? Like, in this conversation, we've been emphasizing a lot of breaking the problem down into all these pieces that we're going to solve independently. But the most important part is when we put it all back together. Yeah. And scientists are actually using these things, you know, how is it going and what are we doing, right? And so I think the AI for science program is critical for us to get that feedback and be closing the loop with people that are using these things every day in the lab. And so I am super excited about that.
我认为有一点非常重要，值得一提，它说明了为什么在Anthropic工作如此令人兴奋，并且与Anthropic的理念完美契合。我们在讨论加速和增强能力的同时，另一面是确保安全，以及我们所有人都肩负着巨大的责任，要确保我们在提高模型能力和推出更具影响力的产品时，以负责任的方式进行。这符合我们的责任扩展政策和生物安全社区的最佳实践。这是我非常关心的事情。
▶ 英文原文 ⏱
One other point that I think is really important to make that, you know, speaks to why Anthropic and why the experience of doing this within Anthropic is so exciting, it's such a perfect fit, is that as we're talking about accelerating and enhancing capabilities, right? The other side of that is safety and the tremendous responsibility that we all have to making sure that we are improving the model's capabilities and releasing, you know, increasingly impactful products in a way that is responsible and aligned with our responsible scaling policy and best practices in the biosecurity community. It's something I care deeply about.
我在生物安全领域工作了多年，我认为在大多数公司，提升生物学模型的商业目标与其影响之间往往会有一些紧张关系，对吗？同时也要考虑到安全和责任方面的问题，比如在需要的时候放慢速度，确保我们谨慎行事，并具备所有必要的安全措施。但在Anthropic，我们没有这种紧张关系。这就是我们公司的DNA。我认为这在这里非常有价值。这种理念对生命科学领域的每个人来说也都很熟悉，尤其是那些开发治疗方法和医疗技术的人。
▶ 英文原文 ⏱
I've worked in biosecurity for years and I think that, you know, at most companies, right, there would be some tension between the impact and the commercial aims of making these models better in biology, right? And the safety and responsibility side of, you know, slowing down when we need to and making sure that we're being careful and have all the right safeguards in place. But at Anthropic, we don't have that tension, right? That's our DNA as a company. I think that's so valuable here. It's also really familiar, you know, to everyone in the life sciences, right, for people that are developing therapeutics and medical technologies, right?
一方面，你有产品开发部门和商业目标。另一方面，你有质量管理体系，这是一套管理所有操作的程序和实践，以确保操作的安全性。我认为，这种方法自然契合于我们的人工智能策略，确保强大的人工智能在开发过程中顺利且安全，这与生命科学领域的需求一致。这让我非常兴奋，我认为这也是我们作为该领域合作伙伴的重要特质。
▶ 英文原文 ⏱
On the one hand, you have your product development arm and your commercial goals. And on the other hand, you have a quality management system, right, which is a set of procedures and practices that govern everything you do in order to make sure that you're doing so safely, right? And so I think it's just such a natural fit, you know, our approach here to AI of making sure that developing really powerful AI goes well and is done safely and what needs to happen in the life sciences, right? And so that's something that I'm personally really excited about that I also think is a big part of who we are as a partner in this field.
对，就是一种假设，对吧？我们必须那样做。我们欠自己的，欠科学家的，欠世界的，就是要认真对待这些问题。是的，我还常常想到另一件事：从根本上讲，从我们的DNA来看，我们就是一家研究机构。并不是所有的人工智能公司都能这样说，比如Frontier Labs等。但我觉得作为一家研究机构，这让我们能够以一种创造共享感、共同的目标和合作方式，来与研究人员、实验室和其他研究组织互动。
▶ 英文原文 ⏱
Right, yeah, it's an assumption, right? Like, we have to do that. We owe it to ourselves. We owe it to scientists. We owe it to the world to take those sorts of questions really seriously. Yeah, and I think the other thing that I think about a lot is, like, at our core, at our DNA, we're a research organization. I don't think you can say that about all other AI companies, you know, Frontier Labs, et cetera. But I think being a research organization allows us to engage with researchers, labs, other research organizations in a way that really creates kind of a shared sense of, like, ownership and goals and working together.
好的，我们想推动这些技术发展，让它们充分发挥作用和潜力，我们真的很投入地推动这件事。我觉得我们很幸运能有这样的情况。我非常直观地感受到，我们的创始团队、领导团队以及整个组织的各个层级和团队中有很多人都是科学家，对吧？很多人有过相关培训，也有很多人天生和性格上就适合做科学家。我认为你可以从我们所做的工作中感受到这种氛围。这让我们很自然地去与其他科学家合作，有点像“让猴子来管理动物园”一样，因为我们有如此多热爱科学的人在引领这个团队。
▶ 英文原文 ⏱
Right, like, we want to advance the technologies and see them put to, you know, the full purpose and power and are really invested in seeing that forward. Yeah, I think we're really lucky that that's the case. And, you know, I feel that very viscerally, that so many people on our founding team and our leadership team and just throughout all levels and teams in the organization are scientists, right? Many by training, many by nature and disposition. And, you know, I think that you can feel that sort of, you know, in all the work that we do. It makes it, you know, so natural to just go out and get to work with other scientists and all these things. And, you know, it's a little bit like, you know, the monkeys are running the zoo, right, where we have people that are so passionate about science driving the ship.
我认为这意味着我们可以享受很多乐趣，是的。但同时，它也关乎对核心问题的重视，比如安全问题和对力量的理解。此外，还有一些核心问题，比如什么是真正应该解决的问题，以及对科学为何艰难、以及是什么拖慢了科学进展的认识。如果我们需要在10年内取得相当于100年的进步，那么这到底是什么样的过程呢？解开科学的奥秘，有些事情仅仅是了解文献，我们可以每天花整天时间来研究。实际上，我认为很多科学家可能都希望能每天花整天时间来阅读文献。但即便如此，你在任何时刻也只能了解已发表或预印文章的一小部分。所以，要全面跟上进展几乎是不可能的，对吧？但Claude可以做到。
▶ 英文原文 ⏱
And I think that it means that we get to have a lot of fun. Yeah. But I think it also, it's a lot of fun, but also that appreciation for core questions like safety and understanding what the power is. And also core, you know, questions about like what are the right problems to solve and, you know, an appreciation for what makes science hard, what slows science down, you know, if we need to make 100 years of progress in 10, you know, what does that actually look like, right? And, you know, you can draw back the veil of science and, you know, there are some of those things of just understanding the literature, right? Like you can spend all day every day. As a matter of fact, I think a lot of scientists would probably love to spend all day every day reading the literature. But like even then, you'd get through, you know, some small, tiny fraction of what was published or pre-printed at any given moment. So it's just impossible to keep up, right? But Claude can keep up.
好的，好的。那么在最后，我们来聊聊生命科学领域的未来吧。我们之前谈到了生物信息学编码、临床工作以及一些早期合作伙伴展示的不同工作。我们还可以思考一下如何构建和发展新的合作关系，以不断提高模型的能力。当你开始考虑未来时，你会怎么做呢？
▶ 英文原文 ⏱
Yeah. Yeah. Okay. So let's talk a little bit maybe here at the end about, you know, the future of life science work. And, you know, we've talked about bioinformatics encoding. We've talked about some, you know, clinical work and different work that has been like demonstrated by some early partners. And then maybe also just ways that we're thinking about, like, building this up and continuing to develop new partnerships, push the models towards greater capabilities. Where do you go when you start to think about the future?
好的。在开始考虑未来的时候，我认为首先我们需要确保克劳德具备所有生物领域科学家应该具备的基础知识。也就是说，像理解蛋白质结构生物学这样的事情，并且能够查看有机化学中的分子，理解其结构和功能等。在建立了这些基础之后，我认为我们可以追求一些非常令人兴奋的目标。其中一个我尤其喜欢谈论并认为很关键的，是克劳德实际上学会在实验室中执行实验。
▶ 英文原文 ⏱
Yeah. So when we start to think about the future, you know, I think first we need to make sure that Claude has all of the foundational knowledge that any scientist in the bio world would have, right? So things like understanding protein structural biology, right? And being able to look at a molecule from organic chemistry and understand its structure and function and things like that, right? And so once you establish that base, then I think there's some really exciting places that we can go after that. Where one of the ones that I really like to talk about and that I think is critical is Claude actually learning to execute experiments in the lab, right?
我认为，为了实现我们都想要的那个世界，这一步是必须要走的。长久以来，我们在这个领域取得了很多进展，但可能没有达到一些人期望的程度。这就是在实验室中实现琐碎工作自动化的愿景。然而，我相信现在是可以实现的。我认为，这是一个非常重要的领域，我们必须深入研究和关注。试着停下来想象一下，等到那一天来到时，我们的生活会是怎样的场景。那一定会非常令人惊叹，对吧？
▶ 英文原文 ⏱
I think in order to get to this world where, you know, we're all going, that needs to happen. And again, this is a problem that for so long, you know, we've been making a lot of progress, maybe not as much as some had hoped for, right? In terms of this vision of automating the tedious work of life in the lab. But I believe that it's possible now. And I think that that's a really important area where we have to drill in and focus on. And I think, you know, just pause for a moment as to what life will be like when we get there. It'll be incredible, right?
我们将能够从与Claude讨论一个实验开始，到与Claude一起设计实验计划，再到让Claude起草实验协议。而且，你可以在这些步骤之间来回交流。然后，当你准备好了，你可以说，好吧，现在去进行那些实验，我会在早上查看数据。我认为这是闭合循环并实现我们所说的加速过程的关键。此外，我认为对我们未来研究来说，另一个非常重要的主题是在生物学领域，就像在科学的任何领域一样，我们有机会直接从自然的真实数据中学习。
▶ 英文原文 ⏱
We'll be able to go from, you know, talking to Claude about an experiment to designing an experimental plan with Claude to having Claude draft the protocols. And, right, you can go back and forth on them. And then when you're ready, you can say, all right, now go run those experiments and I'll review the data right in the morning. And so I think that's critical for closing the loop and enabling that acceleration that we're talking about. And the other thing that I think is a really important theme for our future research is in biology, as with any domain in science, we have the opportunity to learn directly from real data from nature, right?
所以，一方面，我们大量依靠人工创建的标注和其他由人类整理或创建的数据集进行模型训练和学习。但这里有个机会，可以通过高通量生物测量来实现实验室参与的主动学习。而生物学在这方面非常适合的另一个原因是，我们每年在实验数量上都呈现出一种增长规律，也就是我们能够在单位时间内执行的实验数量持续增加。
▶ 英文原文 ⏱
And so on the one hand, we do a lot of model training and learning on annotations that are created by humans and other data sets that are either curated or created by humans, right? But there is an opportunity here to really do sort of lab-in-the-loop active learning from high-throughput biomeasurements. And the other reason why bio is such a good fit for that is we really, you know, are every year on a scaling law of the number of experiments, right, per unit that we can do, right, in terms of the throughput of these systems.
所以，这就是我越来越感到兴奋的两个主题。当我们开始思考如何在这些任务中超越人类能力时，我们迟早会从人类专家那里学到所有能学的东西。答案是从实验室获取数据。
▶ 英文原文 ⏱
So those are two themes that I'm increasingly excited about where, you know, when you start thinking about how do we move beyond human capabilities in these tasks, right, at some point we're going to saturate learning from human experts. The answer is to get the data from the lab.
是的，我认为这是一个很棒的主题。还有另一点我想提到的是，我觉得在现有的能力和使用方面仍然存在巨大的差距。其中一个让我印象深刻的就是把Claude引入课堂和基础培训中，真正深入地实施，让许多科学家都在使用Claude。
▶ 英文原文 ⏱
Yeah, I think this is a great theme. And the other thing that maybe I would point to is I think there's still this huge overhang, if you will, in terms of, like, current capabilities and use. And one of the things that sticks out to me is, like, starting to get Claude in the classroom in basic training, like, really, you know, kind of implemented in a deep way such that, you know, many scientists are using Claude.
此外，这种体验和产品开始有一种非常协调统一的感觉。在这里，Claude不仅是一个虚拟助手，更是一个虚拟科学家，能够协助解决问题。换句话说，Claude 不仅仅回答问题，而是帮助科学家，解决任何问题。好的，Eric，这次谈话真是太棒了。说到科学，总是很有趣，因为我们还有很多工作要做。
▶ 英文原文 ⏱
And also that experience and the product, you know, starts to have this very cohesive feel where Claude is that virtual assistant and that virtual scientist that is helping not answer a problem but, you know, answer a scientist, answer any problem. All right, Eric, this has been awesome. I mean, it's always fun to talk science since we've got a lot of work to do.
谢谢你抽出时间来参与，我们非常期待Claude的未来，生命科学，以及向前沿迈进的发展。是的，谢谢你，Joda。这真的很有趣，而且我们才刚刚开始。我们是的。
▶ 英文原文 ⏱
So thanks for taking the time and really looking forward to the future of Claude, life sciences, and pushing towards the frontier. Yeah, thank you, Joda. This has been a lot of fun, and we're just getting started. We are.