2024-03-05 NVIDIA Corporation (NVDA) TD Cowen 44th Annual Health Care Conference (Transcript)

NVIDIA Corporation (NASDAQ:NVDA) TD Cowen 44th Annual Health Care Conference March 5, 2024 9:50 AM ET

英伟达公司（纳斯达克：NVDA）TD Cowen 第 44 届年度医疗保健大会 2024 年 3 月 5 日上午 9:50

Company Participants 公司参与者

Kimberly Powell - Vice President, Healthcare

金伯利·鲍威尔 - 副总裁，医疗保健

Conference Call Participants

电话会议参与者

Matt Ramsay - TD Cowen

马特·拉姆齐 - TD Cowen

Matt Ramsay 马特·拉姆齐

Good morning, everybody. Thank you for, attending this session and, obviously, coming to the 44th Annual TD Cowen Healthcare Conference. My name is Matt Ramsay. I actually run the semiconductor practice and research, so I'm a bit of a fish out of water here. So, I, guarantee you I know less about healthcare than anybody in this room. But, hopefully, we're going to have a good discussion about AI and healthcare. And been super pleased to be joined by Kimberly Powell, who runs the, healthcare business for NVIDIA. You might have heard of NVIDIA? It's been a little bit topical in the last few months. I can't have a conversation without on any topic literally without AI. Healthcare is certainly no different. But, Steve and my friend, J.J., we're going to do some, Q&A with Kimberly, and, hopefully, explore a little bit about what NVIDIA is doing in the healthcare market, across a number of verticals. So, Kimberly, thank you for joining again this year. We really appreciate it.

大家早上好。感谢大家参加本次会议，显然，参加第 44 届年度 TD Cowen Healthcare Conference。我叫马特·拉姆齐。我实际上负责半导体业务和研究，所以在这里有点格格不入。所以，我向你们保证，我对医疗保健的了解比这个房间里的任何人都少。但是，希望我们能就人工智能和医疗保健展开一场良好的讨论。非常高兴与负责 NVIDIA 医疗保健业务的金伯利·鲍威尔一起参加。你可能听说过 NVIDIA 吧？在过去几个月里，它有点热门。无论谈论任何话题，我都无法避免谈论人工智能。医疗保健当然也不例外。但是，史蒂夫和我的朋友 J.J.，我们将与金伯利进行一些问答，希望探讨一下 NVIDIA 在医疗市场上的业务，涉及多个垂直领域。金伯利，感谢你再次加入今年的会议。我们非常感激。

Kimberly Powell 金伯利·鲍威尔

Thank you. It's a pleasure to be here. I mean, this conference is just amazing. The sessions are very much painting the picture of what we're so excited about these next 10 years. So, pleasure to be here and thank you so much for the invitation.

谢谢。很高兴能在这里。我的意思是，这次会议真是太棒了。这些讨论非常生动地描绘了我们对接下来的 10 年感到如此兴奋的画面。所以，很高兴能在这里，非常感谢邀请。

Matt Ramsay 马特·拉姆齐

So I think I wanted to start, Kimberly, just, for this audience, I spend in my tech world, I spend all day talking about the hardware stack of NVIDIA, the software stack of NVIDIA, what you guys are doing at CUDA, and libraries on top and driving the data center business towards what could almost be $100 billion in revenue this year, which is pretty astounding. No one's really seen that happen before. But I want to see if you could tell us a bit about how the healthcare franchise fits in with the broader company NVIDIA.

所以我认为我想要开始，金伯利，就是为了这个观众，我在我的技术世界中度过了一整天，谈论了英伟达的硬件堆栈，英伟达的软件堆栈，你们在 CUDA 上的工作，以及顶层的库和将数据中心业务推向今年几乎可以达到 1000 亿美元收入的方向，这是相当惊人的。以前没有人真正看到过这种情况发生。但我想知道你是否能告诉我们一些关于医疗保健特许经营如何与英伟达这家更广泛的公司相契合。

So, what technologies you're deploying? What verticals within the healthcare industry are you trying to influence and infect, and then, like, how you go about building a healthcare franchise on the side of a huge tech technology company?

那么，您正在部署哪些技术？您在医疗保健行业内的哪些垂直领域试图影响和感染，然后，您又如何在一家庞大的科技公司旁边建立医疗保健特许经营权？

Kimberly Powell 金伯利·鲍威尔

No. I appreciate the question. I'll take you back a little bit. I'm working on my sixteenth year at NVIDIA. So, we've been working in the healthcare space for a decade and a half. And it was right at the time that we were pioneering a new computing approach called accelerated computing. We built these graphics, GPU's, graphics processing units for gaming. And if you think about what gaming is doing, it's simulating lights. We built a very, very powerful processor and we realized that this processor could be used for a ton of different applications, but we didn't know them all. And so, Jensen, our CEO made the decision, let's invite somebody to go figure that out. And that's kind of been my charter ever since. So we're pioneering accelerated computing, back in 2008. And some of the earliest applications of our accelerated computing, technology were born right here at MGH down the street, working in the areas of medical imaging.

不。我感谢这个问题。我会稍微回顾一下。我在英伟达工作已经第十六年了。所以，我们在医疗领域已经工作了十五年。正好在我们开创一种新的计算方法——加速计算的时候。我们为游戏构建了这些图形处理器单元。如果你考虑一下游戏的功能，它在模拟光线。我们构建了一个非常强大的处理器，我们意识到这个处理器可以用于大量不同的应用，但我们并不知道所有的应用。所以，我们的 CEO Jensen 做出了决定，让我们邀请某人去弄清楚这一点。从那时起，这就成了我的任务。所以我们在 2008 年开始开创加速计算。我们加速计算技术的最早应用之一就诞生在附近的 MGH 医院，主要用于医学影像领域。

And if you think about what is, imaging? It's really the cornerstone of all patient care, right? The first thing we need to do is early detection. We need to understand what's going on inside the body and so the tools that we use to use that are largely in the area of imaging. And now imaging drives everything we do inside the walls of healthcare.

如果你考虑一下什么是成像？这真的是所有患者护理的基石，对吧？我们需要做的第一件事是早期检测。我们需要了解身体内部发生了什么，所以我们用来做这件事的工具主要是成像领域。现在成像驱动着我们在医疗保健领域内所做的一切。

And so, on accelerated computing platforms, you're essentially creating sensor technologies, whether that's CT, ultrasound, MRI, the new photon counting CT that is incredibly opening up new applications of being able to see the function of the anatomy, microscopy, pathology. All of these, think of them as sensors. And they need to have very, very powerful computers that allow you to take that sensor data and present it as information that can really drive decision-making for clinicians. And so accelerated computing, imaging, some of the very first applications and we're still pioneering new imaging applications today.

因此，在加速计算平台上，您基本上是在创建传感器技术，无论是 CT、超声波、MRI，还是新的光子计数 CT，这些技术极大地开拓了能够看到解剖结构功能、显微镜、病理学等新应用。所有这些，都可以看作是传感器。它们需要非常强大的计算机，让您能够将传感器数据呈现为可以真正推动临床医生决策的信息。因此，加速计算、成像，一些最早的应用，我们今天仍在开拓新的成像应用。

The second thing that NVIDIA has been pioneering with the world over the last decade is artificial intelligence. Back in 2012, a new modern approach [audio gap] to neural networks, leveraged, was made possible by our accelerated computing platform, but really spawned off this new computing approach called artificial intelligence. And right now, today, we're living in these two platform shifts of computing that are enabling whole new classes of applications. And so, yes, we are built on top of the foundational pieces that NVIDIA does every single day, people know us for chips, absolutely, our GPUs. We also build CPUs. We also have DPUs for data processing units or smart NICs. The complete system of computing is now something we've pioneered to allow for this next wave of artificial intelligence we're all living in, which is generative AI.

NVIDIA 在过去十年里与全球一起开创的第二件事是人工智能。早在 2012 年，我们加速计算平台实现了一种新的现代方法[音频间隙]来利用神经网络，从而产生了一种被称为人工智能的新计算方法。而今，我们正处于这两种计算平台转变的时代，这使得全新类别的应用程序得以实现。是的，我们建立在 NVIDIA 每一天都在做的基础部分之上，人们认识我们是因为芯片，绝对的，我们的 GPU。我们也制造 CPU。我们还有用于数据处理的 DPU 或智能 NIC。现在，我们开创的计算系统已经为我们所处的下一波人工智能浪潮铺平了道路，这就是生成式人工智能。

And so these platform shifts are a complete new way of doing computing. And we recognize that, that applications are going to be built in very, very different ways. And so, our job in the healthcare business unit, to talk about what is our mission statement, it's how do we take these pioneering computing approaches and apply it to the healthcare domain. In fact, because of generative AI now, we see this as the opening of an industry that is going to be the world's largest technology industry. Healthcare will be the largest technology industry in the coming years.

因此，这些平台转变是一种全新的计算方式。我们意识到，应用程序将以非常非常不同的方式构建。因此，我们在医疗保健业务部门的工作是谈论我们的使命宣言，即我们如何将这些开创性的计算方法应用到医疗保健领域。事实上，由于生成式人工智能的出现，我们认为这是一个即将成为世界上最大技术产业的开端。医疗保健将成为未来几年最大的技术产业。

At the end of the day, you don't know a lot about healthcare, but every single one of us is a patient fact. We are all part of the health care ecosystem, whether we're patients or actually part of delivering care. And so generative AI is giving these incredible new capabilities of opening new aspects of healthcare and to become technology.

在一天结束时，你可能对医疗了解不多，但我们每个人都是一个病人事实。无论我们是病人还是实际上是提供护理的一部分，我们都是健康护理生态系统的一部分。因此，生成式人工智能正在提供这些令人难以置信的新能力，开启医疗保健的新方面，并成为技术。

Let me give you some examples of where we're, super, super excited. One is still in the area of medical devices and med tech. Every single day, new sensor technologies are being born or new applications are being able to be built with imaging. We're pioneering foundation models for imaging. You can do full segmentation of a 3D scan in seconds. Full segmentation, you used to have to have humans who would sit there and contour the images so that they could prepare the plans for image guided therapy. You can now have a computer completely do that automatically, so that clinicians can actually think about the plan a lot more than the arduous work of segmenting, for example.

让我给你举一些我们非常非常兴奋的例子。一个领域仍然是医疗设备和医疗技术。每一天都有新的传感器技术诞生，或者新的应用程序可以通过成像构建。我们正在为成像开创基础模型。您可以在几秒钟内对 3D 扫描进行完整分割。完整的分割，您过去必须让人类坐在那里并轮廓图像，以便他们可以为图像引导治疗准备计划。现在您可以让计算机完全自动执行这项工作，这样临床医生实际上可以更多地考虑计划，而不是费力地进行分割工作，例如。

So, imaging, medical technology, exciting area. Imaging is also at the cornerstone of what we're seeing in minimally invasive and robotic surgery, right? You're sticking something, essentially a camera inside the body so you can give the clinicians much more visibility without having to have the effects of open surgery.

因此，成像、医疗技术是一个令人兴奋的领域。成像也是我们在微创和机器人手术中看到的基石，对吧？基本上，您将摄像头置于体内，这样可以使临床医生在无需进行开放手术的情况下获得更多的可见性。

And then we're moving into robotic surgery, where you now have actually robots assisting the surgery. And being able to again use imaging and artificial intelligence to augment what the clinicians are seeing in real time. We are pioneering complete full stack computing platforms for being able to do that real-time AI called NVIDIA Holoscan. And this is the capability to literally take a millisecond image information and overlay AI on top so that a clinician can interact with that data in real time. A lot of this is leveraged, if you can imagine, from the self-driving car industry. It takes your mind a minute to think about this, but a self-driving car and a surgical robot are actually very, very similar in the job and the task that they do and the computing platforms that they need.

然后我们将进入机器人手术领域，现在实际上有机器人协助手术。并且能够再次利用成像和人工智能来增强临床医生实时看到的内容。我们正在开拓用于实时 AI 的完整全栈计算平台，名为 NVIDIA Holoscan。这就是能够将毫秒级图像信息与 AI 叠加在一起，以便临床医生可以实时与数据进行交互的能力。很多这些技术都是借鉴自无人驾驶汽车行业。想象一下，这需要一分钟时间来思考，但无人驾驶汽车和外科手术机器人实际上在工作和任务以及所需的计算平台上非常相似。

So we're able to leverage a lot from these other core businesses of NVIDIA and apply it in here. So medical devices, imaging, medtech, surgical robotics, incredibly exciting area. And you're going to see this next phase of digital surgery coming. And the other area that we're super, super excited about is artificial intelligence and accelerated computing for drug discovery. We see this as the -- a sort of next generation of computer-aided drug discovery is in front of us.

因此，我们能够充分利用英伟达的其他核心业务，并将其应用在这里。因此医疗设备、成像、医疗技术、外科机器人等领域都非常令人兴奋。您将看到数字手术的下一个阶段即将到来。我们非常非常兴奋的另一个领域是人工智能和加速计算用于药物发现。我们认为这是计算辅助药物发现的下一代。

For the very first time, now that we have three things that's happened. The life sciences industry has created the digital biology moment. You have CRISPR technologies, cryo-electron microscopy, next-generation sequencing, spatial genomics, all creating these gigantic data sets of biology, ingredient number one. You have GPT, generative pretrained transformers, the ability to learn from large amounts of data and represent data inside the computer.

第一次，现在我们有三件事情发生了。生命科学行业创造了数字生物学时刻。您拥有 CRISPR 技术、冷冻电子显微镜、下一代测序、空间基因组学等，所有这些都创造了这些巨大的生物数据集，这是第一个要素。您还拥有 GPT，生成预训练变换器，能够从大量数据中学习并在计算机内部表示数据。

GPT and the third ingredient, which is AI supercomputers that allow for you to take that digital biology information, the method of generative pretrained transformers and representing that information in a computer and on supercomputers to train these new models. And so this is the very first time we're able to take the language of drugs, DNA sequence of characters, just like our English language, but just in three billion characters long and four letters, amino-acid sequence, again, 20 different characters in a sequence, chemistry SMILES strings, also another sequence. So you can imagine we can now represent the language of drugs inside of a computer.

GPT 和第三个成分，即 AI 超级计算机，允许您获取数字生物信息，使用生成预训练变换器的方法在计算机和超级计算机上表示该信息，并训练这些新模型。因此，这是我们第一次能够将药物语言、DNA 字符序列，就像我们的英语一样，但只有 30 亿个字符长和四个字母的氨基酸序列，再次是 20 个不同字符的序列，化学 SMILES 字符串，也是另一个序列。因此，您可以想象我们现在可以在计算机内部表示药物语言。

And so we see this as the next generation of computer-aided drug discovery is in front of us, and there's a lot, a lot of evidence about that. So these are some of the areas that we're very, very excited about.

因此，我们认为计算机辅助药物发现的下一代就在我们面前，有很多证据证明这一点。因此，这些是我们非常激动的一些领域。

Question-and-Answer Session

问答环节

Q - Matthew Ramsay Q - 马修·拉姆齐

Obviously, a lot going on. I just wanted to ask one more question for myself and I'll turn it over to my health care colleagues. If you just think about the scale of your health care franchise today, right, can you talk a little bit about the size of the investments you're making within the company? Obviously, you're leveraging the technology from the data Center Group. You mentioned the automotive group, But what's -- I mean, anything you can give us on the size of it from a revenue perspective, from an investment perspective, just what is the health care franchise inside of NVIDIA?

显然，有很多事情要处理。我只想为自己再问一个问题，然后将话题转交给我的医疗保健同事。如果你仅仅考虑一下你们今天医疗保健业务的规模，你能谈谈你们在公司内部所做的投资规模吗？显然，你们正在利用数据中心集团的技术。你提到了汽车集团，但是从收入角度、投资角度，你能给我们一些关于规模的信息吗？在英伟达内部，医疗保健业务的规模是怎样的？

Kimberly Powell 金伯利·鲍威尔

Yes, we've talked about this recently. So the health care business is well over now $1 billion, both direct and indirect through our partners. So it's really ramping up now. We've been preparing for this moment for the last decade and a half. We have been hiring deep domain expertise. We have everything from applied research, dedicated applied research teams, you can see them on a lot of the publications that are happening in this area.

是的，我们最近讨论过这个问题。因此，医疗保健业务现在已经超过了 10 亿美元，直接和间接通过我们的合作伙伴。所以现在真的在加速发展。在过去的十五年里，我们一直在为这一时刻做准备。我们一直在招聘深入领域专业知识。我们拥有从应用研究到专门的应用研究团队的一切，您可以在这一领域发生的许多出版物上看到他们。

We have dedicated engineering teams that go across the platforms that we're building, and I'll talk a little bit more about NVIDIA Clara after Clara Barton is our health care platform that has those different domains of computing platforms within it, who invented the American Red Cross, we think of that as a platform to really facilitate these new computing approaches to the entire industry. So dedicated engineers. And now we have a lot of products. You're going to see, we have our GPU Technology Conference in about a week and a half, and you're just going to see a ton of new products largely software, and that's what is interesting.

我们有专门的工程团队负责我们正在构建的各个平台，我将稍微谈一下 NVIDIA Clara，Clara Barton 之后是我们的医疗保健平台，其中包含不同领域的计算平台，她发明了美国红十字会，我们认为这是一个真正促进整个行业采用这些新的计算方法的平台。所以有专门的工程师。现在我们有很多产品。您将看到，我们将在大约一周半的时间举办 GPU 技术大会，您将看到大量新产品，主要是软件，这才是有趣的地方。

The Healthcare business unit, as I say, is the application of computing in this industry. So application synonymous with software. Most of what we build is software. We did decide to build a hardware computing platform for the real-time imaging, and that is NVIDIA Holoscan on IGX. And that is because we needed it. The industry needed it. They needed to have a general purpose computer that can do that real-time processing. But otherwise, it's all about that. And so we have thousands of people around the company doing everything from applied research, engineering, the product, all of the go-to-market.

医疗保健业务部门，正如我所说，是在这个行业中应用计算技术。因此，应用程序与软件是同义词。我们大部分所构建的是软件。我们决定为实时成像构建硬件计算平台，即 NVIDIA Holoscan on IGX。这是因为我们需要它。这个行业需要它。他们需要一个通用计算机来进行实时处理。除此之外，一切都是关于这个的。因此，我们公司有成千上万的人员从事应用研究、工程、产品以及所有的市场推广工作。

We have an inception program. It's our start-up program, AI start-up program, kind of a virtual accelerator at NVIDIA. And I was just telling somebody about it. We have about 3,000 AI health care start-ups in that inception program, and we are supporting them all over the world. They are everything from stealth, they were just a professor and they have a great idea, all the way up to pre-IPO. And we do everything from early access to our technology, to making them much more effective in leveraging the technology and even all of the go-to-market that we're doing together. Am I not loud enough? Is this better, okay. So pretty big -- a decade and half we've been building it, and we're ready to see this next decade and a half with some real pioneering work.

我们有一个起始计划。这是我们的创业计划，AI 创业计划，在 NVIDIA 有一个虚拟加速器。我刚刚告诉某人。我们在那个起始计划中有大约 3,000 家 AI 医疗创业公司，并且我们在全球支持它们。它们从隐形开始，他们只是一位教授，有一个很好的想法，一直到上市前。我们做的一切，从早期获取我们的技术，到使他们更有效地利用技术，甚至我们一起进行的所有市场推广。我不够大声吗？这样好一点。所以相当大——十五年来我们一直在建设，我们准备看到接下来的十五年，有一些真正的开拓性工作。

Matt Ramsay 马特·拉姆齐

Awesome. Josh, I don't know, Kimberly started going down the path of like experience from imaging through surgical and postsurgical, I know that's an area that you've spent a lot of time on, so if you have any questions.

太棒了。乔什，我不知道，金伯利开始走上像从影像到手术和术后经历这样的道路，我知道这是你花了很多时间的领域，所以如果你有任何问题。

Unidentified Analyst 未知分析师

Thanks for the mic. I appreciate the time here today. I think medical device companies have been working on for years is incorporating AI and machine learning into digital robotics or ecosystem that consists of next-generation robotics, world-class instrumentation, advanced imaging visualization and data analytics and digital solutions powered by AI and ML. But in order to accomplish the optimal approach to the interventions, and so any -- I mean, just from NVIDIA standpoint, any stance on where we are in terms of the plan where robotics and AI-driven analytics will guide physicians to the best next steps intraoperatively or intra-procedure. And I know now NVIDIA has a big platform that's helping a number of partners get there. But that's maybe just a foundational question.

感谢麦克风。我很感激今天在这里的时间。我认为医疗器械公司多年来一直在努力将人工智能和机器学习融入到数字机器人技术或生态系统中，这些生态系统包括下一代机器人技术、世界级仪器、先进成像可视化和数据分析，以及由人工智能和机器学习驱动的数字解决方案。但为了实现干预的最佳方法，所以任何 - 我的意思是，就英伟达的立场而言，关于我们在计划中的位置，机器人技术和人工智能驱动的分析将指导医生在手术过程中或术中采取最佳的下一步措施。我知道现在英伟达有一个大平台，正在帮助许多合作伙伴实现这一目标。但这可能只是一个基础性问题。

Kimberly Powell 金伯利·鲍威尔

Sure. Thank you. So we think of this whole area as the transition it's going through right now as you think of a lot of these medical device companies as being hardware companies. And the reason we invented NVIDIA Holoscan is to create an industry of software-defined medical devices. So that, as you know, being a clinician, this is the practice of medicine. It's not just the singular AI that can diagnose something. There are decision points that are happening continuously, especially in surgery.

当然。谢谢。所以我们认为这整个领域正在经历的转变，就像你认为很多这些医疗器械公司是硬件公司一样。我们发明 NVIDIA Holoscan 的原因是为了创造一个软件定义医疗器械的行业。因此，正如你所知，作为临床医生，这是医学实践。不仅仅是能够诊断某些东西的单一人工智能。尤其是在手术中，决策点是持续发生的。

And so what do you need in order to do that, you need a real-time AI computing platform, and you need it to be continuously be able to take new applications that are coming to it. Just like your Tesla car is getting over-the-air updates, every morning improving its performance, we have now created the conditions for the medical device and surgical robotics industry to take advantage of that, okay? So one, you needed the compute platform for these AIs to be able to land, live on and deliver the cognitive experience so that decision-making can be made better.

那么，为了做到这一点，您需要一个实时的人工智能计算平台，并且需要它能够持续接收新的应用程序。就像您的特斯拉汽车正在接收空中更新一样，每天早上都在提高性能，我们现在已经为医疗设备和外科机器人行业创造了条件来利用这一点，好吗？因此，首先，您需要计算平台，使这些人工智能能够落地、生存并提供认知体验，以便决策能够更好地进行。

Everything from overlaying information don't cut their tool tracking, so that you can understand the phase of surgery, all of this incredible information now can be built into these systems. And it's not only that. There are new foundation models, everything that you're seeing with GPT Midjourney, these other applications that you're seeing in the consumer Internet space of multimodal models, we're now seeing -- we're now right at the cusp of new models called vision language models. So you're incorporating the ability to watch video and temporally reason over that video about what's happening.

从叠加信息到不切割他们的工具跟踪，这样你就可以了解手术阶段，所有这些令人难以置信的信息现在可以构建到这些系统中。而且不仅如此。有新的基础模型，你所看到的一切都是与 GPT Midjourney 一起看到的，这些其他应用程序是你在消费者互联网空间看到的多模型模型，我们现在看到了——我们现在正处于被称为视觉语言模型的新模型的前沿。因此，您正在将观看视频的能力和对视频发生的事情进行时间推理结合起来。

In fact, there's some incredible new research where you can watch a surgery and predict what's going to happen in the next 20 seconds. So imagine the decision-making that's going to transform within the walls of that operating room. That whole operating room is going to become an AI, whether you're the anesthesiologist, whether you're the nurse going for the next tool or whether you're actually the surgeon who's thinking about what his or her next move is for this particular patient. You're going to be able to talk to a computer and say, "Could you tell me about any pre-existing diseases that I should know about because the surgery is going a little bit long and I need to know any comorbidities that might change or affect or complicate the surgery." You could ask a computer to say, please bring me up her preoperative MRIs, so that I can see if I've extracted everything that I need to during the surgery.

事实上，有一些令人难以置信的新研究，您可以观看手术并预测接下来 20 秒会发生什么。因此，请想象一下在手术室内将发生的决策过程。整个手术室将变成人工智能，无论您是麻醉师、下一个工具的护士，还是正在考虑下一步怎么做的外科医生。您将能够与计算机交流，并询问：“您能告诉我有关任何既往疾病的信息吗？因为手术进行得有点长，我需要了解可能会改变、影响或使手术复杂化的任何合并症。”您可以要求计算机说：“请给我看她的术前 MRI，这样我就可以看看在手术过程中是否提取了我需要的一切。”

So the amount of information that's going to now be able to enter the OR, the ability because now we have all these systems, automatic speech recognition, large language models to take that speech recognition, go retrieve information from all the hospital systems, the OR and not even just the OR, but the OR is a very exciting space where we're going to be able to start presenting that information in a much more friendly way for these clinical decision makings.

因此，现在能够进入手术室的信息量将会增加，这是因为我们现在拥有所有这些系统，自动语音识别，大型语言模型可以利用语音识别获取信息，从所有医院系统中检索信息，手术室不仅仅是手术室，而且是一个非常令人兴奋的空间，我们将能够以更友好的方式呈现这些临床决策所需的信息。

So this idea of intraoperative information is extremely exciting. And then you're going to see it on both sides of it, pre-operatively to prepare you, to remind you how does this robot work? What does that button do? How might I use that? What tool should I be using? You're going to have a conversation to refresh yourself, to retrain yourself. And then post-operatively, you're going to be able to use these artificial intelligence applications to summarize it to reason over how could I deliver this surgery better to do all of the unfortunate paperwork that a lot of our clinicians have to do during their pajama time, if you will, right? Because wouldn't it be great if 90% of what they have to summarize and reports and otherwise is done by a computer, and they're taking only the 10% to check it and make sure it's accurate and move on with their day. So it's going to span end-to-end artificial intelligence, and that's why these systems need to be built.

因此，手术过程中信息的这个想法非常令人兴奋。然后你会在两方面看到它，术前准备，提醒你这个机器人是如何工作的？那个按钮是做什么的？我该如何使用它？我应该使用什么工具？你将进行一次对话来让自己恢复，重新培训自己。然后手术后，您将能够使用这些人工智能应用程序对其进行总结，以推理如何更好地进行手术，完成许多我们的临床医生在睡衣时间必须完成的不幸文件工作，对吧？因为如果 90%的总结和报告等工作都由计算机完成，他们只需花费 10%的时间来检查并确保准确性，然后继续他们的一天，那不是很好吗？因此，这将涵盖端到端的人工智能，这就是为什么这些系统需要被构建的原因。

Unidentified Analyst 未知分析师

Maybe one more follow-up just on NVIDIA Clara Holoscan, already, in fact platforms are already bringing real-time decision making to a number of different treatment settings. And I mean, I would imagine that medical device manufacturers are clamoring to partner up. Any help just understanding how many medical device manufacturers can get access to the platform and partner with NVIDIA. And anything you can share that's in the public domain about who those some of those partners are today?

也许再谈谈 NVIDIA Clara Holoscan，实际上，平台已经将实时决策带入了许多不同的治疗环境。我想，医疗设备制造商应该急于合作。请帮忙了解有多少医疗设备制造商可以访问该平台并与 NVIDIA 合作。您能分享一些公开领域中有关今天一些合作伙伴的信息吗？

Kimberly Powell 金伯利·鲍威尔

Yes, I appreciate it. One of the early adopters of NVIDIA Holoscan was actually Medtronic. We announced our partnership with Medtronic. They see this computing platform as their future ability to innovate in a software-defined way. The very first application is their FDA-approved AI colonoscopy, they published last year that, that AI allows them to see 50% more polyps, which obviously they want to take care of during the procedure so that they don't develop into any cancer and that is the first application.

是的，我很感激。 NVIDIA Holoscan 的早期采用者之一实际上是美敦力公司。我们宣布了与美敦力公司的合作伙伴关系。他们将这个计算平台视为未来创新的软件定义方式。第一个应用是他们去年发布的获得 FDA 批准的人工智能结肠镜检查，这种人工智能使他们能够看到更多的息肉，显然他们希望在手术过程中处理这些息肉，以免发展成癌症，这是第一个应用。

And now they have the ability to continuously add applications to it. And so this is exactly what we're building for the industry. Instead of a Medtronic or a GE Healthcare otherwise having to reinvent, rearchitect, redesign the computing system itself, write all the system software, write the real-time application framework, NVIDIA has embodied. We've codified the last sixteen years of our experience into this computing platform so that rapid innovation can happen on top of it. And so we have well over 30 partners that is working with the Holoscan platform, everything from the sensor technology to get the data in. So 4K, 240-hertz cameras that just bring amazing fidelity when you're doing any kind of this robotic surgery, for example, ultrasound, lots of sensor technology partners.

现在他们有能力不断向其中添加应用程序。这正是我们为该行业构建的内容。与 Medtronic 或 GE Healthcare 等公司必须重新发明、重新架构、重新设计计算系统本身，编写所有系统软件，编写实时应用程序框架不同，NVIDIA 已经实现了这一点。我们已经将过去十六年的经验编码到这个计算平台中，以便快速创新可以在其上进行。因此，我们有超过 30 个合作伙伴正在使用 Holoscan 平台，从传感器技术到获取数据。因此，例如，进行任何类型的机器人手术时，超声波、大量传感器技术合作伙伴带来了惊人的保真度。

We have hardware partners as well. So we create the system architecture. They can actually build the compute system so that it fits in an endoscopy rack or it goes inside your robot. And then we have, of course, a whole bunch of solution development partners. So people that can facilitate the actual application development in a lot of the medical certifications. Now we've built the system so that it's medical grade in mind. So both the hardware is 60601 architected, the software stack is 62304 if you guys are familiar with these. And again, this is in an effort to take a lot of heavy lifting away or reinvention of the wheel inside of these otherwise hardware companies that are looking to transform their businesses into Software-as-a-Service companies.

我们也有硬件合作伙伴。因此，我们创建了系统架构。他们实际上可以构建计算系统，使其适合内窥镜架或放入您的机器人中。然后，当然，我们有大量的解决方案开发合作伙伴。因此，可以促进在许多医疗认证中进行实际应用开发的人员。现在，我们已经构建了系统，以便考虑到医疗级别。因此，硬件是按照 60601 架构设计的，软件堆栈是 62304，如果您熟悉这些的话。再次强调，这是为了减轻许多重复劳动或在寻求将业务转变为软件即服务公司的这些硬件公司内部重新发明轮子的努力。

And this is a tremendous uplift for them. I mean you see the economics that have gone on with the autonomous vehicle market, the absolute same economics, if not much, much more will happen in the area of health care. We simply don't have enough health care professionals to serve the community of patients. We need to make health care more equitable. We need this technology to reach more of the world. I mean, still about one-third of the world's population has access to imaging or surgery, right? And so the only way we're going to get there is through artificial intelligence platform. So we've done a ton of the heavy lifting on that side.

这对他们来说是一个巨大的提升。我是说，你看到了自动驾驶车辆市场的经济状况，绝对相同的经济状况，如果不是更多，将会在医疗保健领域发生。我们简单地没有足够的医护人员来服务患者群体。我们需要使医疗保健更加公平。我们需要让这项技术覆盖更多的世界。我是说，全球约三分之一的人口才能获得影像或手术服务，对吧？所以我们唯一能够实现这一目标的方式就是通过人工智能平台。因此，我们在这方面已经做了大量的繁重工作。

We have over 40 reference applications out on GitHub in the open source. So you can literally get applications up and running in a matter of minutes. So it's a tremendous platform. Medtronic is an early adopter. We also partnered with a really great start-up out of France, Moon Surgical. They've gone from -- they're a very young company. 2019 is when they started. They have approved essentially robotic-assisted devices on the market built on NVIDIA Holoscan in just a few 18 months to 2 years. And that's the kind of acceleration factor that you're going to see in robotic surgery.

我们在 GitHub 上有超过 40 个参考应用程序是开源的。因此，您可以在几分钟内轻松启动和运行应用程序。这是一个非常强大的平台。Medtronic 是早期采用者。我们还与法国一家非常优秀的初创公司 Moon Surgical 合作。他们是一家非常年轻的公司。2019 年是他们的创立之年。他们在市场上推出了基于 NVIDIA Holoscan 的批准的基本上是机器人辅助设备，仅用了 18 个月到 2 年的时间。这就是您将在机器人手术中看到的加速因素。

No longer should it take five to eight years to get a new type of device to market because you have the compute platform already sort of architected, we should really, really see a massive innovation cycle there. So super excited about these next couple of years. And just like the AV market took off, we're kind of right at that moment where they're realizing that they can -- these med tech companies are realizing AI is right there for them to take and grab and be able to innovate and drive new business models for their companies.

不再需要花费五到八年的时间将新型设备推向市场，因为您已经对计算平台进行了架构设计，我们应该真的能够看到一个巨大的创新周期。对接下来的几年感到非常兴奋。就像 AV 市场腾飞一样，我们正处在那个时刻，他们意识到他们可以——这些医疗科技公司正在意识到 AI 就在眼前，可以利用并创新，推动公司的新商业模式。

Matt Ramsay 马特·拉姆齐

Super interesting. I wanted to shift the conversation a little bit because we got the little blinky devices that we only have 8 minutes. But the -- our team in the semiconductor franchise and the tech franchise has done a couple of 400, 500-page reports over the last couple of years with our biotech team on AI and drug discovery. And I mean I've spoken with Jensen, NVIDIA's Founder about this a little bit and talking about even things that go down to the individual patient's genome as a absolute first variable in drug discovery in certain cases, right? So Steve, I know that's an area that you've spent a ton of time on. So if you have any questions, particularly, that would be great.

超级有趣。我想稍微改变一下谈话的方向，因为我们有这些小闪烁设备，只有 8 分钟。但是 - 我们半导体特许经营团队和技术特许经营团队在过去几年里与我们的生物技术团队一起完成了几份 400、500 页的报告，涉及人工智能和药物发现。我是说，我已经和 NVIDIA 的创始人 Jensen 谈过一点关于这个话题，甚至讨论到在某些情况下，将个体患者的基因组作为药物发现中的绝对第一变量。所以 Steve，我知道这是你花了大量时间的领域。如果你有任何问题，尤其是关于这方面的问题，那就太好了。

Unidentified Analyst 未知分析师

Yes. No, that would be great. Yes. No, I appreciate you doing this. So I was told years ago by Sean McClain at Absci kind of an early adopter of your chipset. The chips are going to be kind of the coin of the realm in terms of AI and drug discovery. But maybe just give us a sense of how broad and extensive your partnerships in the AI drug discovery is mostly in terms of like numbers. I mean I appreciate you gave the $1 billion reference. And then maybe can you talk about how those customers are using your chips maybe beyond the usual better jobs on goal, reduce time lines, clinical candidates, any interesting ways, they are using your chips?

是的。不，那将是很棒的。是的。不，我感谢你做这个。所以多年前，Absci 的 Sean McClain 告诉我，他是你芯片的早期采用者。这些芯片将在人工智能和药物发现领域成为主要的货币。但也许可以让我们了解一下，你们在人工智能药物发现领域的合作伙伴关系有多广泛和深入，主要是从数量上来说。我的意思是，我感谢你提到了 10 亿美元的参考。然后也许你能谈谈这些客户如何使用你们的芯片，也许超出了通常的更好的工作目标，缩短时间表，临床候选人，任何有趣的方式，他们如何使用你们的芯片？

Kimberly Powell 金伯利·鲍威尔

Yes, exactly. So part of the NVIDIA Clara platform, we invented the ability to -- it's called NVIDIA BioNeMo. And this is an application framework that is speaking the language of biology. So just like I described, we needed to take what was the tools today that you use for natural language processing, and we needed to them speak the language of biology. Three billion long sequence has a much different architecture going into a computer than our spoken word.

是的，确切地说。所以在 NVIDIA Clara 平台的一部分，我们发明了一种能力——它被称为 NVIDIA BioNeMo。这是一个应用框架，它说的是生物学的语言。就像我描述的那样，我们需要将今天用于自然语言处理的工具转化为生物学的语言。三十亿个长序列进入计算机的架构与我们口头表达的方式完全不同。

So we had to understand the data formats, there's different model architectures. They're GPT like, but they're not GPT. When you go and you train a foundation model on, let's say, DNA, RNA or otherwise, they're different model architectures. And so we're taking the state-of-the-art model architectures, and we're making them available in this application framework. And then finally, what we're doing is there are literally many papers every single day now, every single day, many of them popping out of MIT, but also out of Meta and otherwise on foundation models and biology.

因此，我们必须了解数据格式，有不同的模型架构。它们类似于 GPT，但并非 GPT。当您训练一个基础模型，比如 DNA、RNA 或其他模型时，它们有不同的模型架构。因此，我们正在采用最先进的模型架构，并将其提供在这个应用框架中。最后，我们正在做的是现在每天都有很多论文，每天都有很多论文涌现出来，其中许多来自麻省理工学院，但也来自 Meta 等其他机构，涉及基础模型和生物学。

And so what we're doing is creating the ability for this industry, the biotech industry, the biopharma industry, who are not AI scientists, they're not computer scientists, but be able to leverage the AI and computer sciences going on, taking the models that are being built state-of-the-art models and we're turning them into cloud services. So literally, a biologist can become an AI biologist just by logging into a website. So this is going to be a huge exponential in terms of their ability to access and leverage this technology.

因此，我们正在创造这个行业的能力，生物技术行业，生物制药行业，他们不是 AI 科学家，也不是计算机科学家，但能够利用正在进行的 AI 和计算机科学，采用最先进的模型，并将其转化为云服务。因此，一个生物学家只需登录网站，就可以成为 AI 生物学家。这将极大地提高他们获取和利用这项技术的能力。

So we built BioNeMo, the application framework and we build it for two purposes. So one way that we're partnering with the industry is most of the data in biotech and biopharma is proprietary data as it should be. And so we want to create the capabilities for them to create their own foundation models. Because essentially, what you're doing with foundation models is you're creating your IP, you're representing your company's intelligence in a model. You want that to be yours. Some of these companies go back to 300 years.

所以我们建立了 BioNeMo，这个应用程序框架，我们建立它有两个目的。我们与行业合作的一种方式是，生物技术和生物制药领域的大部分数据都是专有数据，这是应该的。因此，我们希望为他们创建自己的基础模型提供能力。因为基本上，您使用基础模型所做的是创建您的知识产权，您在模型中代表您公司的智慧。您希望那是您自己的。其中一些公司可以追溯到 300 年前。

All of that data is actually part of the company's DNA or part of their intelligence. And so now you can codify, you can represent that into these foundation model so that they can continue to do their work. So the building of these models, large-scale training. So Amgen, for example, is one of the early adopters of BioNeMo. They had all of their proprietary and very useful antibody data. They're at the top of the tier for antibody design and they wanted to create foundation models so that they could think differently about how they do the drug discovery process.

所有这些数据实际上是公司的 DNA 的一部分或者是他们的智能的一部分。现在你可以对其进行编码，将其表示为这些基础模型，以便他们可以继续进行工作。因此，构建这些模型，进行大规模训练。例如，安进公司是 BioNeMo 的早期采用者之一。他们拥有所有专有和非常有用的抗体数据。他们在抗体设计方面处于领先地位，并希望创建基础模型，以便他们可以以不同的方式思考如何进行药物发现过程。

They thought they were going to take six months to a year to develop these foundation models. They developed five models in about four weeks on our platform. They took these models, they integrated them into their antibody design process. And I love the readouts that Amgen is doing and actually a lot of the pharma companies are starting to do it. They took their design make test process that -- and then they measured it. It's about two years long, and the output is about a 50% clinical candidate likelihood, okay?

他们以为他们需要花费六个月到一年的时间来开发这些基础模型。他们在我们的平台上大约四周内开发了五个模型。他们拿这些模型，将它们整合到他们的抗体设计流程中。我喜欢安进正在进行的结果分析，实际上很多制药公司也开始这样做。他们采用了他们的设计制造测试流程，然后进行了测量。这个过程大约需要两年时间，输出结果是大约 50% 的临床候选可能性，好吗？

That's their traditional approach. They inserted generative AI upstream of a lot of the work that they do. They use generative AI to generate new proteins, new ideas outside of the scientist thinking box, they use generative AI to predict their properties so that they're manufacturable, that they're non-toxic, all of the things that you need to do, and then they put it into their lab. And this is coming into the industry really in the view of this idea of lab in the loop.

这是他们的传统方法。他们在很多工作中插入了生成式人工智能。他们使用生成式人工智能来生成新蛋白质、新想法，超越科学家的思维框架，他们使用生成式人工智能来预测这些属性，以便能够制造，无毒，所有你需要做的事情，然后他们把它放到他们的实验室里。这实际上是在这种实验室循环的观念中进入行业。

Generative AI lab in the loop where you use generative AI to really help you generate new ideas, but fine-tune those ideas, then put them in the lab, everything, every data point that comes out of the lab, inject it back into the models and you kind of go in this iterative, active learning cycle and what Amgen read out now is they're getting to reduce that time of design make test to two years to nine months, and they're also increasing the clinical likelihood from 50% to 90%. And this is the idea behind our excitement in generative AI is this ability to, what I say, is play the game, not the score at this moment.

生成 AI 实验室中，您可以使用生成 AI 真正帮助您生成新的想法，然后微调这些想法，将它们放入实验室，每一点数据，每一个来自实验室的数据点，注入回模型中，您会进入这种迭代的、积极学习的循环，Amgen 现在的结果是，他们将设计、制造、测试的时间缩短到了两年到九个月，同时将临床可能性从 50%提高到了 90%。这就是我们对生成 AI 的兴奋之处，这种能力，我所说的，是在这一刻玩游戏，而不是看分数。

有可能已经成为现实，比如LLY，这是跨代的能力，会进一步拉大某些地方的差距。

Playing the game means how can we change our strategy around drug discovery, so that, yes, we can increase our chances of the ability to enhance what's going into the clinic to have a better success rate. I don't even think it's about the time. It's not even about the time, right? Because at the end of the day, this has to be safe and effective in clinical trials. And so what we're really trying to do is open the search space, right, because it's essentially an infinite search space and fine-tune our ideas by using these generative AI methods.

玩游戏意味着我们如何围绕药物发现改变策略，以便我们能增加提高进入临床试验的机会，从而提高成功率。我甚至认为这不是关于时间。对吧？因为归根结底，这必须在临床试验中安全有效。因此，我们真正想做的是打开搜索空间，因为这本质上是一个无限的搜索空间，并通过使用这些生成式人工智能方法来微调我们的想法。

So Amgen is an early adopter. Late last fall, we announced our Genentech partnership. We work with all of the large pharma in Japan. We helped Mitsui build a supercomputer called Tokyo-1 that Astellas and Ono and Daiichi use and we're helping them leverage the supercomputer to build foundation models. So we have over 50 partners throughout the biopharma ecosystem are actually using BioNeMo and then we have some of the deeper partnerships that we've talked about Amgen and decode for foundation models in biology.

安进是一个早期采用者。去年秋末，我们宣布了与基因泰克的合作伙伴关系。我们与日本所有大型制药公司合作。我们帮助三井建立了一个名为东京-1 的超级计算机，阿斯利康、大塚和第一三共正在使用，并帮助他们利用超级计算机构建基础模型。因此，我们在整个生物制药生态系统中有超过 50 个合作伙伴实际使用 BioNeMo，然后我们还有一些更深入的合作伙伴关系，我们已经谈到了安进和解码生物学基础模型。

We are just at the early innings, and that's the exciting part. The enthusiasm is fantastic and the readouts that are coming from the pharma companies are showing this has real value in terms of their drug discovery process. And that's when we say the words, the next generation of computer aided drug discovery, I think the whole process of drug discovery is transforming because AI is being injected in different places, and we're rethinking the serial process and looking at active learning in the loop process to really facilitate drug discovery.

我们只是在比赛的早期阶段，这正是令人兴奋的部分。热情是极好的，制药公司发布的结果显示，这在他们的药物发现过程中具有真正的价值。当我们说到下一代计算机辅助药物发现时，我认为整个药物发现过程正在转变，因为人工智能正在被注入到不同的地方，我们正在重新思考串行过程，并关注循环过程中的主动学习，以真正促进药物发现。

Matt Ramsay 马特·拉姆齐

That's super fascinating stuff. I just want to say it's a pleasure to be in front of this audience. Thank you, Kimberly, and your team for coming and spending some time with TD Cowen. It's not too often you see a $2 trillion company, where they're telling you they're just entering their largest market. So it's going to be an exciting time. And Kimberly has brought a few team members from her health care team here at the conference. I'm sure there'll be about, so you guys can all meet them. But we really do appreciate the partnership, Simona, for you as well. And thank you so much, Kimberly, for your time.

这是非常迷人的事情。我想说很高兴能站在这个观众面前。谢谢你，金伯利，以及你的团队前来与 TD Cowen 交流。很少见到一个市值 2 万亿美元的公司，他们告诉你他们刚刚进入了他们最大的市场。所以这将是一个令人兴奋的时刻。金伯利从她的医疗团队带来了一些团队成员参加这次会议。我相信大家都会在这里见到他们。但我们真的很感激合作，Simona，也感谢你，金伯利，为你的时间。

Kimberly Powell 金伯利·鲍威尔

Thank you, Matt. Steve, Josh, thank you so much. Thank you. Appreciate it.

谢谢，马特。史蒂夫，乔什，非常感谢你们。谢谢。感激不尽。

2024-03-05 NVIDIA Corporation (NVDA) TD Cowen 44th Annual Health Care Conference (Transcript)

2024-03-05 NVIDIA Corporation (NVDA) TD Cowen 44th Annual Health Care Conference (Transcript)

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