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Uber earnings + using AI to revolutionize clinical trials w/ Unlearn.AI’s Charles Fisher | E1734

发布时间 2023-05-03 17:17:09    来源

摘要

(0:00) Jason kicks off the show (1:21) Uber’s Q1 earnings (8:31) QuickNode - Get one month free by using code TWIST at https://go.quicknode.com/twist (9:49) The foundation of Unlearn.AI (12:42) Unlearn.AI’s impact on the clinical trial process (14:24) Criticisms of the current clinical trial model (16:24) ML’s Impact on drug discovery (24:16) CacheFly - Get 10 terabytes free by signing up at https://twist.cachefly.com (25:42) The data used in the medical system today (34:05) Microsoft for Startups Founders Hub - Apply in 5 minutes for six figures in discounts at http://aka.ms/thisweekinstartups (35:24) Unlearn.AI’s business model (40:22) Building a foundational model for health (44:06) The pace of AI today vs. the previous decade (46:35) More on Unlearn.AI’s business model (48:15) Creating foundational datasets in health Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp Check out Unlearn.AI: https://www.unlearn.ai/ FOLLOW Charles: https://twitter.com/charleskfisher FOLLOW Jason: https://linktr.ee/calacanis Thanks to our partners: Listen here: Apple: https://podcasts.apple.com/us/podcast/this-week-in-startups-audio/id315114957 Spotify: https://open.spotify.com/show/6ULQ0ewYf5zmsDgBchlkr9 Overcast: https://overcast.fm/itunes315114957/this-week-in-startups-audio More from us: Twitter: https://twitter.com/twistartups Instagram: https://www.instagram.com/twistartups Official site: https://thisweekinstartups.com Subscribe to our YouTube to watch all full episodes: https://www.youtube.com/channel/UCkkhmBWfS7pILYIk0izkc3A?sub_confirmation=1 Subscribe to TWiST Clips for all the best moments: https://www.youtube.com/channel/UCS7tJlcUA6PzVHEMo-X7ddg?sub_confirmation=1 #startups #entrepreneurship #investing #angelinvesting #tech #news #business

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中英文字稿  

Alright everybody, it's a great day for your boy, J-Cow. I am still long. Uber, I still have a large amount of holdings and the stock was up massively today because they had record high-freak cash flow and they beat on revenue.
大家好，今天是我J-Cow的好日子。我仍然持有Uber的大量股票，股价今天大幅上涨，因为他们创下了现金流的历史新高，并且在收入上超过了市场预期。

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This is a great day for me. You can see I'm very enthused. My net worth went up but also the bet I placed over a decade ago that defined my career as an investor. I always believe this company would start becoming a money printing machine and thus that has happened to you on revenue 8.8 billion. That's up 29% year over year.
对我来说，今天是个伟大的日子。你可以看到我非常兴奋。我的净资产增加了，但是我在十多年前下的一注赌注也定义了我作为投资者的职业生涯。我一直相信这家公司将开始成为一个印钞机，现在它的收入已经达到了88亿美元，同比增长了29%。

And we're talking about cloud computing at Alex on last week and we're talking about oh it's falling to single digits. Well you know it's not falling to single digits. Uber up 29% year over year. Revenue came in 100 million above analyst estimates according to CNBC. The total trips in q1 2.1 billion. I remember when Uber had a total of three riders on the platform and we'd done maybe a dozen rides up 24% year over year. That's incredible and this includes mobility and delivery monthly active platform customers.
上个星期我们在Alex谈论了云计算，并且我们谈到了它正在跌至个位数。但是实际上并没有跌至个位数。据CNBC报道，Uber的年增长率为29%。其收入比分析师预测高出1亿美元。第一季度总共完成了21亿次出行。我还清楚地记得，当时Uber平台只有三名乘客，我们只做了约十几次出行。这一年的增长率达到了24％，这太不可思议了。这一数字包括了每月活跃的出行平台及交付客户。

Max Max Max. Max there's no way to pronounce map sees map sees actually map sees works monthly active platform customers. This is Uber's sort of mouse monthly active users. But what they want to say is these are customers who are active on the platform not dormant accounts once that actually did something. I was 130 million that is extraordinary. We're talking nine figures worth of customers monthly and looking at the revenue 72% revenue growth for mobility 23% year over year for delivery and freight the freight business drop 23% year over year. I'm not sure what the details are there will double click on it in a future episode. Can't win them all to edit three and bad but that's a small number. It's an emerging business for them that they're investing in total gross bookings right this is the top line before they give a large percentage of the money they make to the drivers and the drivers are doing spectacular.
马克斯，实际上没有办法发音的是“地图查看器”。“地图查看器”实际上是指Uber每月活跃平台客户的数量。他们想要表达的是这些顾客在该平台上活跃而不是休眠帐户，曾经真正做过一些事情。这是惊人的，有1.3亿的客户。我们谈论的是每个月数以亿计的客户，考虑到营收增长72％的出行业务和23％的年度增长的送货和货运业务，货运业务的收入下降23％。我不确定其中的细节，在未来的一个问题中会重点讲解它。不能全都赢，有三个出纰漏，但这是个很小的数字。对于他们正在投资的新兴业务，这是一个可见的业务，在总毛收入方面，这是他们支付给司机之前的收入。司机的表现非常好。

That's why so many people are driving for Uber don't believe the fake news which keeps saying like Uber drivers are making eight dollars or nine dollars an hour. That's all made up nonsense. The truth is they're making 25 35 dollars an hour. And the gross bookings worth 31.4 billion of 19% year over year. So the revenue grew 29% year over year but gross bookings grew only 19% which means Uber's more profitable and charging more for their services. Just great to see bottom line they had a small net loss of 157 million and that included a 320 million dollar benefit from net unrealized gains related to Uber's equity investments but really cash flow that's what matters right how much cash makes it into the coffers as we say and this includes capital expenditures right so you'll have some things on the books that are capital expenditures but the actual amount of cash into the business so this is accounting issues. Record high free cash flow 549 million dollars cash and cash equivalents and short term investments 4.2 billion for the team over at Uber and I think the really interesting part of all this is lifts demise. They are a shrinking amount of this industry and Uber I don't want to say has a monopoly because it's not a monopoly of door dash out there you have lift out there.
这就是为什么有这么多人在开Uber，不要相信那些说Uber司机每小时只能挣8美元或9美元的假新闻。那都是胡说八道，事实上他们可以挣25美元到35美元每小时。今年Uber净营收314亿美元，同比增长19％。尽管收入同比增长了29％，但毛收入仅增长了19％，这意味着Uber更具盈利性，并收取更高的服务费。最终，Uber净亏损额仅为1.57亿美元，其中包括与Uber股权投资相关的320万美元的未实现收益。然而，真正重要的是现金流，即公司实际赚到的现金净额，包括资本支出在内的所有费用。虽然会计问题存在，但Uber的自由现金流创下了历史新高，达到了5.49亿美元，而现金和短期投资额也达到了42亿美元。最值得关注的是竞争对手Lyft的衰落，他们逐渐在这个行业中失去份额，而Uber不仅没有垄断市场，还有DoorDash和Lyft等竞争对手。

Paragraph 1: You have people competing like public transportation and micro mobility and people owning their own cars rent a cars and taxi and livery driver so to say Uber has a monopoly or they don't on mobility nor do they have it on delivery. What they have is they have the majority now of app driven rides and they have a strong presence I think their number two in the United States behind door dash in delivering groceries and food but people order from other sources as well right there they're still instacard you still have Amazon to live in groceries and whole food so it's not quite a monopoly but it's a strong position in those areas and the average map see did 14 rides or food orders in the quarter that's almost five per month which is very impressive because you take the 2.1 billion trips you divide it by 150 million you know map sees just taking a guess here that maybe they're taking 14 rides or food orders in the quarter which would be about five per month that's the average now that means there's people who use Uber a lot there's people who drop in but if you're like me I'm using Uber Eats and taking Uber rides at least 10 times 15 times a month so I think I'm probably in the 40 or 50 a quarter 200 a year kind of group as a family because man my daughters love to they get me every time all we did our homework can we get boba or you know can we order sushi and yeah I'm a sucker because they just take it out and I'm just like you know what I want you have a great child and enjoy some nice sushi and some boba so just to Dara and the team and Dara's coming on the show in over the summer and we'll have a great interview and catch up but the stock is ripping it's up almost 11% today which puts me in a great mood not just because money which is nice but enough of that it's just about being right and betting on a team and really seeing the investment come to fruition.
在公共交通、微型出行和私人车主租赁车辆、打车和豪华车租赁公司之间，没有人拥有出行或配送的垄断地位，因此说Uber具有垄断地位是不准确的。他们现在拥有大多数应用驱动的乘车，我认为他们在美国排名第二，在配送杂货和食品方面强劲的存在，但人们也会从其他渠道进行订购，比如Instacart、亚马逊订购杂货和全食超市等，因此它并不完全是垄断，但在这些领域仍处于强势地位。平均而言，每个乘客在这一季度中乘坐Uber或订购食品14次，几乎每月5次，这非常令人印象深刻，因为如果你将210亿次出行除以1.5亿的乘客数，平均每个乘客可能在这个季度中乘坐Uber或订购食品14次，这意味着有人经常使用Uber，也有人很少使用，但如果像我一样经常使用Uber Eats和Uber乘车，每月至少10至15次，那么我想我可能是一个每季度40或50次、每年200次的常客族群，因为我的女儿喜欢点奶茶或是订寿司，我很喜欢她们，所以我就给她们订了。至于Dara和他的团队，Dara将在暑期上节目，我们将进行一次真正的采访，这家公司的股票正在飞速上涨，今天涨幅接近11%，这让我很高兴，不仅因为钱，而是看到我们的投资成果实现了。

Paragraph 2: At this year at Founder University I'll make somewhere between 50 and 125 K investments as a tribute to that 25 K investment I made in Uber as one of the first investors maybe the third or fourth I don't know so if you want $25,000 from me to start your company get two or three founders have one technical person make an MVP come to Founder.University hang out with me we're gonna have a we're gonna actually have our own space in San Mateo soon and just come hang out with Jacob let me give you that lucky 25 K first check so you can incorporate maybe come to our accelerator the launch accelerator will give you a hundred thousand and I'll let us syndicate you on the syndicate.com share it with other angel investors and get you a millie or two I think the average is like 700 K to the syndicate so it's been an amazing journey with Uber as my best investment in history and I'm trying to hit another one or a bigger one and so that's not going to be easy but in the next 10 years I hope to invest in maybe two or 300 names per year which would put me at 2,000 more investments in my second decade of investing to go with the 300 my first 250 in my first decade I'm gonna 10 next that and you know you never know maybe I had another Uber or two and maybe that's you so Founder.University or launch.co slash apply to meet with our team great job team Uber all right next up on the program Charles Fisher the CEO of unlearn AI.
在今年的创始人大学，我会进行50到125千美元的投资，以纪念我在Uber作为最早的投资者之一投资了25千美元的经历，我可能是第三或第四个投资者，我不知道。所以，如果你想从我这里获得25,000美元来创立自己的公司，你需要有两到三个联合创始人，其中一个技术人员，并打造一个MVP。来参加创始人大学，和我一起玩，我们很快就会在San Mateo拥有属于我们自己的空间，来和Jacob一起玩吧，让我给你这个幸运的25K第一笔支票，这样你就可以注册了，或者来参加我们的加速器——“发射加速器”，我们会给你10万美元，并让其他天使投资者和我们一起投资，最后可能获得一两百万美元，据说“发射加速器”的平均投资金额是70万美元，Uber的投资历程是我最成功的一次投资，我正在尝试命中另一次或者更大的成功，这不容易，但是在未来的10年里，我希望每年能够投资两三百家公司，这样在我的第二个投资十年里，我将进行2000多次投资，加上第一个十年的投资为300次，我会继续下去的。你永远不知道，也许我会找到另一个Uber或两个，而这也是创始人大学或launch.co/apply，来和我们团队会面吧。接下来是unlearn AI的CEO Charles Fisher。

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大家都知道在区块链上建立应用程序的所有抱怨，它很慢、可靠性不高、出现问题时没有支持，对吧？区块链是一项令人难以置信的创新，好消息来了，Quick Node解决了所有这些问题，他们为区块链开发人员提供了无与伦比的可靠性和速度，可以访问18个链和35个网络的无限节点。Quick Node提供惊人的响应时间和全天候专属的客户支持团队，无论规模如何都能提供稳定的性能，API响应速度比竞争对手快2.5倍，拥有最复杂和全球平衡的云和裸金属Web 3架构。听着，大家，这就是Web 3的AWS或Azure，如果你正在建立分散的应用程序(DAPS)，就需要使用Quick Node，就这么简单。了解为什么Twitter、Adobe、Queenbase和OpenSee这样的公司使用它，免费使用代码Twist在go.quicknode.com/twist获取一个月的免费试用，记得使用代码Twist。

Paragraph 4: All right everybody welcome back to our special AI series never seen anything move this fast in the 30 years I've been in the technology business so we are having people on the program 3 4 times a week here at this weekend startups to share what they're working on and why so that we can all keep up with this crazy pace.
好的，大家欢迎回到我们的特别人工智能系列节目。在我从事科技行业的30年间，我从未见过任何东西移动得如此之快。因此，我们在这个周末的创业节目中每周邀请三到四个人分享他们正在进行的工作和原因，以便我们都能跟上这个疯狂的步伐。

We found an interesting guest today his name is Charles Fisher he's the founder and CEO of unlearn dot AI unlearn AI and he is taking AI models to try to speed up clinical trials for pharma companies which seems like a really interesting idea Charles and welcome to the program but also one that has me a bit concerned because using chat GPT 3.5 4 and some of the other tools barred from Google and the core AI tool they're frequently hallucinating and giving wrong data so maybe we could start with a little bit of how long you've been working on this and then getting right to how this is going to change clinical trials and the hallucination problem.
今天我们发现了一个有趣的嘉宾，他的名字叫Charles Fisher，他是unlearn.AI的创始人兼首席执行官。他将使用AI模型试图加快制药公司的临床试验速度，这似乎是一个非常有趣的想法。Charles，欢迎来到本节目，但我也有点担心，因为使用Chat GPT 3.5 4和一些从谷歌和核心AI工具中获取的工具，它们经常产生幻觉并给出错误的数据，所以也许我们可以先谈一下您已经在这个领域工作了多长时间，然后着重讨论这将如何改变临床试验和幻觉问题。

Yeah definitely yeah so thanks for having me how long have I been working on this that's a really interesting question I think how long have I been working on like generative modeling as a as an area because I was an academic researcher before starting unlearn so I don't know like 15 years probably since I've been working on sort of generative AI but we've been at this with on there now for about six years not quite almost six years working on yet developing generative AI to currently speed up problems in clinical trials so like let's make clinical trials biggest bottleneck we want to make that faster but eventually we want to roll this out to think about how we can really sort of revolutionize the way we think about medicine.
肯定是的，谢谢你邀请我来。我一直在思考生成建模领域有多久了，这是一个非常有趣的问题。我之前是一名学术研究员，在成立Unlearn之前，我大概已经从事生成AI这一领域约15年了。但我们现在的实践时间已经接近六年了，一直在开发生成AI来加速临床试验中的问题，因为这是最大的瓶颈。我们想要让这个过程更快，最终我们的目标是革新我们对医学的思考方式。

Turning all of medicine from something that's really today kind of an art form into a real predictive science that's founded on computer science. So let's talk about what is a clinical trial what is the you know state of the art architecture of that because I don't invest in this area but you know some of my contemporaries do and they talk about how incredibly incredibly frustrating and humbling it is to beat the placebo as I've been told which is placebo seem to work 10% of the time 15% of the time they have some efficacy that is not zero in some cases pretty amazing what the placebo can do to people's minds in terms of having an impact versus actually having an impact so maybe the definition of in 2023 what is a clinical trial how does it work today and then how is your software going to change that.
将整个医学从一种艺术形式转变为基于计算机科学的真正的预测科学。那么，让我们谈谈临床试验是什么，它的现代架构是什么，因为我不投资于这个领域，但你知道我的一些同行会投资，并且他们谈论了战胜安慰剂的难度和挫败感，据我所知，安慰剂似乎有时候能够有10％，甚至15％的功效，这些功效有时候是非常惊人的，安慰剂能够对人们的心理起到影响，实际上比真正的药效还要大。所以，也许我们需要定义一下什么是临床试验，今天它是如何运作的，以及你们软件将如何改变它在2023年的定义。

A clinical trial is simply a comparison so it's really it's the same thing as an AB test that you would run in any other area. So I have some new experimental treatment and I want to compare that to usually what is currently available. So placebo usually doesn't mean that you get no treatment at all usually given whatever would you normally get for that particular disease plus a placebo. So you're still receiving some treatment and I just want to know which of these two things is better which is safer which works better like so forth. Typically clinical trials are staged out and so we have three different phases. Phase one is usually done in around 10 or so often healthy people. You give them your experimental drug you just increase the dose until you see too high of dose and then that lets you figure out how much is like a safe dosage.
临床试验其实就是一种比较，它与在其它领域进行的AB测试是同样的东西。我有一种新的实验性治疗方法，想将其与通常提供的治疗方案进行比较。安慰剂通常并不意味着你完全不接受治疗，而是通常接受你在此疾病中通常接受的药物加上安慰剂。因此你仍然接受了一些治疗，我只想知道这两种治疗方式哪个更好，哪个更安全，哪个效果更好等等。通常临床试验分为三个阶段。第一阶段通常在10名健康人中进行。你给他们实验性药物，然后逐渐增加剂量，直到达到过高的剂量以便确定安全剂量。

Then after that you move on to a phase two trial that's usually like around 100 people and this is just an early signal of does this seem like a drug that is worth continuing to pursue.
之后你会进行第二阶段的试验，一般会招募约100人，这只是一个早期的信号，该药物是否值得继续追求。

And then the last thing would be a large phase three clinical trial that's usually around a thousand people and here you're going to randomly assign half of the people to receive your new experimental treatment. You're going to randomly assign the other half to receive the control and then at the end of the trial you're going to compare see if it was better or worse and then you can submit those data to the regulators like FDA to help them make a decision about whether or not your drug should be marketed. Got it.
然后最后一步就是进行大规模的第三阶段临床试验，通常需要约1000个人参与。在此，你将随机分配一半的人接受你的新实验性治疗，另一半接受对照治疗。在试验结束后，你将比较两种治疗的效果，并将这些数据提交给监管机构，如FDA，帮助他们决定你的药物是否可以在市场上销售。明白了。

And so that process, I've heard a lot of criticism of that process objectively. What are the criticisms of that process and then we'll get on to sort of what you're doing?
因此，这个过程我听到了很多客观的批评。那么这个过程的批评是什么，然后我们来谈谈你正在做的事情？

There are million criticisms of the process. Well the major ones. Yeah, sure. Yeah, right. So the first thing that I think we tend to encounter is just the amount of time and cost that goes into running one of these trials.
这个过程有无数的批评，尤其是主要的批评。是的，当然。是的，没错。那么我认为我们遇到的第一件事情就是这个试验需要花费大量的时间和成本。

One of these big phase three clinical trials, just the individual trial itself can cost hundreds of millions of dollars to run and they take some, they often take more than five years, right? So you're talking about spending five plus years on a single experiment and hundreds of millions of dollars and most of the time these trials fail. So the majority of time actually only about 10% of drugs that enter clinical trials end up being successful, so 90% failure rate in clinical trials.
在这些大型三期临床试验中，单个试验本身的成本就可以达到数亿美元，并且通常需要五年以上的时间。因此，您正在谈论在一个单一实验上花费五年以上的时间和数亿美元，而大多数情况下这些试验都会失败。实际上，进入临床试验的药物中，成功率仅约为10％，因此临床试验的失败率为90％。

So you're spending like a decade and hundreds of millions of dollars on an experiment with a 90% failure rate. And would that mean if one out of 10 actually work, we're talking about billions of dollars to get a successful drug to market if you were to look at it as a portfolio of say 10 drugs. That's right. Yeah. So incredibly expensive and incredibly time-consuming.
你们要花费十年和数亿美元进行一个失败率达90%的实验。如果其中有一个成功，那是否意味着对于一组10种药物，需要耗费数十亿美元才能将成功的药物推向市场？是的，非常昂贵，耗时也很长。

I think that there are other things in terms of like we need to get participants to be willing to join and take part in these clinical trials. And then you get into other issues of, you know, certainly issues like placebo control are controversial amongst like patient advocates. Why is it that you're participating typically in a clinical trial?
我认为，在参与这些临床试验方面，我们还需要做其他的事情，比如说让患者愿意加入并参与其中。然后，你会遇到其他问题，比如说像安慰剂对照这样的问题，在患者倡导者之间是具有争议的。通常，你为什么要参加临床试验呢？

Usually that's because you want access to this new experimental therapy. So I think that what people are thinking about and certainly what we're thinking about are ways that we can leverage new technologies to alleviate these problems of the speed and cost of clinical trials, but also align them more closely with what patients want. Got it.
通常这是因为你想要接受这种新的实验性疗法。因此，我认为人们正在思考的，肯定也是我们正在思考的，是如何利用新技术来缓解临床试验速度和成本的问题，但同时更密切地与患者的需求相符。理解了。

So how are you using AI machine learning to test drugs? Because it would seem to me that the human body is complex in many ways. And other ways probably very simple. And interactions are hard. So are you literally running a simulation of, here is this new drug for, I don't know, lowering your cholesterol. And you can model the human body and how it would interact and does this occur in parallel to phase one, two and three, or is this something that is just running a simulation that informs how you would then run these different trials, explain to the audience how this works.
那么，你们如何使用AI机器学习来测试药物呢？因为在我看来，人体在许多方面都是复杂的，而在其他方面可能非常简单。各种相互作用也是很难的。你们是在进行模拟，例如新药物降低胆固醇，你们可以建模人体如何相互作用，这是并行进行的一、二、三阶段的吗？还是这只是一个模拟，用于告知你们如何进行这些不同的试验？请向听众解释一下这是如何进行的。

Sure. So like I said, every clinical trial is a comparison. And what we really want to know is for an individual person, we wish we could tell like what would happen to this person if I could take them and I gave them this new experimental drug and I observed how they respond. And I simultaneously don't give them the drug and I observed how they respond. And so the way to run that experiment is to invent a time machine.
没问题。就像我所说的，每次临床试验都是一次比较。我们真正想知道的是针对一个特定的人，如果我给他们这种新的实验性药物并观察他们的反应，会发生什么。同时，我不给他们药物，并观察他们的反应。那么运行这个实验的方法就是发明一个时光机。

Paragraph 1: "So you give them the drug, you take your time machine back in time to the point you did it and then you don't give it to them and you see what happens, right? You do this comparison. We don't have a time machine. But we do have a computer model."
所以你给了他们药物，你把时间机器带回到你给他们药物的时间点，然后你不给他们药物，你看看会发生什么，对吧？你进行这个比较。我们没有时间机器。但是我们有计算机模型。

Paragraph 2: "And so the whole idea kind of behind what we do is that what we're going to do is for every individual person in a trial, we create a digital twin of that person. And it's a computer model that allows us to simulate what would happen to that individual person. And in our case, in the trials, we're always simulating what would happen if they got the control."
因此，我们所做的核心理念是针对每位试验中的个人，我们创造出一个人的数字孪生。这是一个计算机模型，允许我们模拟会发生在该个人身上的情况。在我们的情况中，我们总是模拟如果他们得到控制会发生什么。

Paragraph 3: "So we don't simulate what would happen if they got this brand new experimental treatment. Only what would happen if they got the existing treatment. And the reason is brand new experimental treatment is not really a machine learning problem, right? Machine learning, we learn from data and we make new predictions, right? So what we can do is we'll have data from like 100,000 patients receiving the current treatment."
因此，我们不模拟如果他们接受了这种全新的实验性治疗会发生什么。只模拟如果他们接受了现有的治疗会发生什么。因为全新的实验性治疗并不是真正的机器学习问题。机器学习是从数据中学习并做出新的预测，对吧？因此，我们可以收集来自接受当前治疗的10万名患者的数据。

Paragraph 4: "And then our task is to give in a new patient, how will they respond to this current treatment? And that's kind of a standard machine learning problem. As opposed to here's a brand new molecule, what will it do to a person? That's a very, that's much harder."
接下来我们的任务是为新患者提供现有的治疗方案，他们对此将作出怎样的反应？这是一个常见的机器学习问题。相反，如果提供一个全新的分子，它对人体会有什么作用？这非常困难。

Paragraph 5: "Yeah. So if I would reflect this back to you in simple plain old English, I'm going to even try to simplify it for me. It's like I'm a five year old kind of situation here. We have 100,000 people who have taken this current cholesterol, lowering drug."
“嗯，所以如果我用简单的老式英语反映给你听，我会试着让自己更加简化。这就像是一个五岁小孩的情况。我们有10万人服用了这种当前的降胆固醇药物。”

Paragraph 6: "Right. You're in the new trial, you're going to get cholesterol, a lowering drug, 2.0. It's completely new. And then there's people who are going to get the placebo. But hey, since we know these 100,000 people's age cholesterol level, BMI, heart rate, whatever battery of information, we can say, hey, Jekal, 52 year old Jekal, 174 pounds, this blood pressure, this cardiovascular fitness level, whatever it happens to be, we take your watch data, I don't know what data is state of the earth these days."
"好的，你在新的试验中，会得到一种全新的降胆固醇药物，称为2.0。还会有一些人得到安慰剂。但是，由于我们已经了解这10万人的年龄、胆固醇水平、体重指数、心率以及其他信息，我们可以说，嘿，52岁的杰克，体重174磅，血压是这个，心脏健康水平是这个，我们可以获取你的手表数据，不知道地球上现在的数据状态如何。"

Paragraph 7: "And okay, yeah, look, we have another out of those 100,000 people, we do have 2,000 people who are just like Jekal. We're going to run a simulation to see what would happen with you on the 1.0 medicine. We're going to take the 2.0 and of course we've got some other group of people who are taking the placebo. Is that about right? What's happening?"
“好的，看，我们有另外2000个像Jekal一样的人，他们是那100,000个人中的一部分。我们将运行一个模拟，看看你在1.0药物下会发生什么。我们将使用2.0，当然还有另一组正在服用安慰剂的人。这样说是正确的吗？这是正在发生的事情吗？”

Paragraph 8: "Yeah, I mean, well, yes. So we're taking this historic, this data from the people that currently exist. We're training this kind of machine learning model on that data. Yeah, and then exactly. So we would predict Jekal comes in, we'd say, what would happen to you if you got the placebo and version 1.0 cholesterol medicine."
“是的，我们正在采集之前人们的历史数据，并用这些数据来训练机器学习模型。这样，当Jekal来接受治疗时，我们可以预测如果他使用安慰剂或其他版本的降胆固醇药物会发生什么。”

Paragraph 9: "And so the interesting thing is, if you take that to the extreme, let's imagine this case where that machine learning model week belt is perfect, makes no mistakes at all. That's not true, but that's just a matter of. Yeah, I mean, if it's 50% correct, it's going to have an extraordinary impact."
因此，有趣的是，如果将这种机器学习模型的准确性推到极致，假设这个模型完美无误，没有任何错误。这是不可能的，但这只是一个假设。如果它的正确率是50%，那么它将会有非常大的影响。

Paragraph 10: "Right, yeah. But this interesting scenario, which you can kind of work backwards from, is, well, if that were very true, then for every patient, I can just give them the experiment, the new 2.0 cholesterol medicine. And I can see how they respond to it. And I don't need any compare, I would not need any real patients receiving a placebo. So if you could sort of get to that point where machine learning models are sufficiently accurate, you get to a world in which you're running clinical trials that don't have placebo groups. It would be 100% of the patients receiving your new experimental treatment and zero patients receiving a placebo."
嗯，是的。但这种有趣的情景，你可以从中反推，假如这是真的，那么对于每个患者，我只需给他们实验的新2.0降胆固醇药物，然后再看他们的反应。我就不需要任何对照组，也不需要真正的患者接受安慰剂。如果你能达到机器学习模型足够准确的境界，你将进入一个没有安慰剂组的临床试验世界。每个患者都接受你的新实验治疗，没有一个患者接受安慰剂。

Paragraph 11: "So that would mean you'd have a clinical trial that's got half as many patients in it, which is way faster and cheaper to run. Also, all of your patients are getting access to this new experimental treatment, which is what they want it. So that future is really great for our customers, the pharma companies, because they get faster trials. Actually, great for whole all of medical research, because you basically speed up medical research twice as fast. It's also great for patients."
这意味着你可以开展一项比原来少一半患者的临床试验，这样做将更快、更便宜。而且，所有患者都可以获得这种新的实验性治疗方法，这正是他们所希望的。因此，对于我们的客户——制药公司来说，这个未来非常棒，因为他们可以更快地进行试验。实际上，对整个医学研究来说也很棒，因为你可以将医学研究加速两倍。对于患者来说，这也非常棒。

Paragraph 12: "It's not perfectly achievable because our models aren't perfect today. And so then we get into this question about how we still run randomized studies where some patients receive placebo is to guard against what you were calling earlier with hallucinations. Right? So we want to make sure that we can guarantee that the clinical trials that we work in produce the right results, even if our models are not perfect."
这并不是完美可行的，因为我们的模型今天还不完美。因此，我们面临的问题是如何仍然进行随机研究，其中一些患者接受安慰剂，以防止你之前所说的幻觉。对吧？所以，我们希望能确保我们参与的临床试验产生正确的结果，即使我们的模型不完美。

Paragraph 1: "It seems to me that you, when a new drug comes out, depending on the corpus of data you have about individuals, you have a lot of them. You can give it a shot and say to the AI, hey, make your best predictions and give me, you know, I mean, depending on computer power available, give me all the possible predictions you could come up with in some reasonable amount that a human could actually compare them and say, predict what will happen with these 2,000 people who are joining the trial as best you can.".
当一种新药问世时，根据您拥有有关个体的数据体系，您可能有很多数据。您可以尝试对AI说：“嘿，尽你最大的努力提供最佳预测，然后给我，我是说，根据可用的计算机能力，在有限的范围内尽可能多地提供预测，以便人类可以比较它们，并且对这2,000个参与试验的人的最佳预测”。。

Paragraph 2: And then give them the trial and then see which sets of thinking the AI got correct. That's also a possibility and would also cost nothing because all you're doing is saying, just make a simulation and be like running a simulation on who's going to win the MBA finals based on the data you have from the regular season. Is anybody doing that as well? Because it seems like it could be a worthy use of time.
然后让它们进行尝试，看看人工智能能正确识别哪些思路。这也是可能的，而且不会花费任何费用，因为你所做的只是像基于常规赛数据预测哪个团队将在NBA总决赛中获胜一样地进行模拟。还有人这么做吗？因为看起来这可能是一个值得花费时间的用途。

Paragraph 3: Yeah, I mean, right, so what we are basically doing again is we're simulating how every single patient in this trial is going to respond if they got the new treatment. We are very interested as well into as drugs come out, incorporating, so this is kind of the future world.
是的，我的意思是，我们基本上正在模拟如果这个试验中的每个患者接受了新的治疗方法，他们会如何做出反应。我们也非常感兴趣在新药推出时将其纳入模拟中，这是未来的世界。

Paragraph 4: So the way I kind of view it, interestingly is that clinical trials are highly regulated area, super scientifically rigorous, right? But we think they're the easiest area actually, the easier than all of the other areas of medicine. And the reason for this is because treatments are randomly assigned to patients. So basically the way that this will work is that the model will make mistakes. It will make those mistakes on patients who are randomly assigned to receive the placebo. And it makes the same mistakes on the patients randomly assigned to receive the treatment. And basically in the end, the mistakes end up canceling up. And so because of that, it's like that particular application is really robust to these mistakes that machine learning models make today.
我认为，临床试验是高度监管和极其科学严谨的领域，不过有趣的是，我们认为它其实比所有其他医学领域都更容易。原因在于，治疗方法是随机分配给患者的。因此，这个模型可能会在接受安慰剂治疗的患者身上犯错误。同样，它也可能会在接受治疗的患者身上犯同样的错误。但最终，错误会互相抵消。因此，这种治疗方法对于机器学习模型目前可能会产生的这些错误非常强大，可以说是很鲁莽的。

Paragraph 5: So everybody, when it comes to the blocking and tackling of running your startup, you don't need to reinvent the wheel. CDNs, aka content delivery networks are the place where startups can really overcomplicate things. You don't need custom authentications or custom codes. Now if you're a startup, you need to just check out cash fly. It's a pure play CDN. And CDNs are literally all they do. So they're the best in the world at it. They've been doing it for over two decades. That's 20 years.
在经营初创企业时，大家不需要重新发明轮子来解决一些常见问题，例如 CDN，即内容传递网络。有些初创企业可能会把这个问题搞得过于复杂，而事实上并不需要进行自定义认证或编码等操作。对于初创企业而言，应该查看一下 Cash Fly，这是一种纯粹的 CDN，专注于 CDN 服务超过 20 年，是全球最好的 CDN 服务提供商。

Paragraph 6: Cash fly makes CDNs simple, effective and secure. Let me say that one more time. Simple? Secure. Effective. Effective. Secure simple, simple, effective, secure. You eventually are going to outgrow the smaller ones that are trying to give you too many discounts.
Cash fly使CDNs变得简单、有效和安全。让我再说一遍。简单？安全。有效。有效。安全简单，简单有效，安全。你最终会发现那些试图给你太多折扣的小型公司已经跟不上你的发展。

Paragraph 7: So you want the best of breed. So don't burn your startup credits using a CDN at one of those larger players. Let cash fly handle it all for you. They will help you deliver your content faster than your competitors. You have to be fast if you want to compete. Whether it's videos, your mobile app, games, content, SaaS, the faster you go with your delivery, the more capable you use your product. So go check out cash fly.
如果你想要最优质的服务，就不应该用那些大公司的CDN来消耗你的创业信用。让Cash Fly来处理它们，他们会帮助你比竞争对手更快地传递你的内容。如果你想要竞争，你必须要快。无论是视频、移动应用、游戏、内容还是SaaS，你的传递速度越快，你的产品就越有能力。所以赶快去看看Cash Fly吧！

Paragraph 8: And twist listeners are going to get 10 terabytes free forever. When you sign up at twist.cashfly.com, that's twist.cac, h-e-f-l-y.com. 10 terabytes are waiting for you for free. Stop what you're doing. Pause the podcast. Go to twist.cashfly.com and get your 10 terabytes for free forever.
听众们可以永久免费获得10TB的存储空间。当你注册twist.cashfly.com网站时，即twist.cac, h-e-f-l-y.com，你可以获得10TB的免费存储空间。停下你正在做的事情，暂停播客，前往twist.cashfly.com并免费获得你的10TB存储空间。

Paragraph 9: Talk about the data. Well, the thing I'm curious about is the data you that is currently used for these trials. My understanding is one of the problems is garbage in, garbage out. You get information from patients if they tell you information. Well, how many drinks do you have a week? Sure. They're like, ah, like two. And it's really 20. So if they're reporting data, it's obviously going to be flawed.
谈论数据。那么，我好奇的是你现在用于这些试验的数据。我的理解是一个问题就是"垃圾输入，垃圾输出"。如果患者告诉你信息，你就可以从他们那里获得有关信息。好吧，你每周喝多少饮料？当然。他们会说，啊，我喝了两杯。但实际上可能是20杯。因此，如果他们报告数据，显然会有缺陷。

Paragraph 10: And then if you look at the data that's available in medical records, well, why do we even have medical records today? It's for billing. Right. And to do with, it has more to do. Am I correct with billing than it does with reality? Is that right? Yeah. Yeah. Yeah. So what data do we actually have that has some truth to it? It feels like wearables are perhaps the whole of your blood tests. You can't fake those. I don't believe.
然后，如果你看看医疗记录中的数据，为什么我们今天要有医疗记录呢？是因为结算。没错。与其有关的更多是结算而不是现实吗？对吗？是的。是的。是的。那么我们实际拥有什么数据是真实的呢？感觉像可穿戴设备或者你的全面血液检测。那些是无法伪造的。我认为是这样的。

Paragraph 11: You can tell me if I'm wrong, but blood tests that are historical, maybe body scans, which I just did the Pernovo body scan and wearables. If I gave you my Fitbit data for 10 years and then my Apple Watch, which I switched to, all of those seem to be like, that's pretty rock solid. So are any of those type of things being currently used in these trials? Or is it still just like they go to people's medical records and they give them a survey to fill out?
你可以告诉我我是否理解有误，历史上的血液测试和身体扫描，比如我刚刚做的Pernovo身体扫描和可穿戴设备，比如我给你我过去10年的Fitbit数据和我现在转用的Apple Watch的数据，这些似乎都是很可靠的。那么这些类型的数据是否正在试验中使用？还是只是像以前那样去查询人们的医疗记录和要求他们填写调查问卷？

Paragraph 1: Well, clinical trials are a really unique space when it comes to data in medicine because it's a research study.
临床试验是医学中数据非常特殊的领域，因为它是一项研究性的研究。

Paragraph 2: So one of the problems with medical records, like if you looked at my medical records, you would see that I've been to the doctor like four times in the last 20 years.
因此，医疗记录存在一个问题，比如，如果你查看我的医疗记录，你会发现在过去20年里我去了四次医生。

Paragraph 3: Is that right? And all of the information in between those dates, just not even there.
这是正确的吗？这些日期之间的所有信息都没有。

Paragraph 4: Like because I didn't go to the doctor, so it's gone. It doesn't exist, right?
就像因为我没有去看医生，所以它已经不存在了对吧？

Paragraph 5: But clinical trials are really different.
但是临床试验确实非常不同。

Paragraph 6: So in a trial, it's set up ahead of time and you define this giant battery of exams that you're going to give to patients.
因此，在试验中，提前设置并定义了一系列要给患者进行的考试。

Paragraph 7: And that always includes things like blood tests. Now it includes other new things.
通常这包括血液测试。现在它还包括其他新事物。

Paragraph 8: There are times where people it's going to be wearables. Sometimes it's going to be imaging, maybe we'll get MRIs.
有时人们会认为可穿戴设备会成为主流。有时可能会使用成像技术，比如进行磁共振成像。

Paragraph 9: It could be full genomic tests like you might get a whole genome sequence potentially.
这可能是像你进行全基因组测序的全基因检测。意思是说可以进行完整的基因组检测，包括全基因组测序。

Paragraph 10: So it could be a huge amount of information. It varies from trial to trial.
这可能是大量的信息。具体情况因案而异。

Paragraph 11: But everyone is going to get this giant battery of tests. And then they're going to come in like once a month for the next year and a half.
但是每个人都将接受这个巨大的测试组合。然后他们在接下来的一年半中每个月来一次。

Paragraph 12: And they're going to make the same battery of tests every month.
他们将每个月进行相同的一系列测试。意思是说，他们会定期进行各种测试来检查某种事物的状态或表现。

Paragraph 13: So regardless of what happens to them, whether or not they're feeling good or they're feeling bad, it doesn't make any difference.
所以，无论他们经历什么，无论他们感觉良好还是感觉糟糕，都没有任何影响。

Paragraph 14: You enroll in the study and you come in like once a month and you get this giant battery of tests.
您参加了这个研究，每个月过来一次进行一系列的测试。

Paragraph 15: So there's actually a huge amount of information about these diseases that is being captured in these clinical trials.
因此，这些临床试验实际上捕获了大量关于这些疾病的信息。

Paragraph 16: So this is an opportunity in two ways.
所以这是一个双重的机会。

Paragraph 17: First of all, we run tons of clinical trials every year.
首先，我们每年会开展数以千计的临床试验。

Paragraph 18: Like as a society, we run a ton of them. Actually, the government runs a ton of them.
就像社会中我们经营很多企业一样。实际上，政府经营了许多企业。

Paragraph 19: The NIH funds a ton of clinical trials.
美国国家卫生研究院资助了大量的临床试验。

Paragraph 20: And all of those data just proof how they collected and they're not used again.
这些数据只是证明了它们被收集了，并且没有再被使用。

Paragraph 21: They're just like, what? And then they just throw them in the dumpster.
他们只是拿起这些东西，什么也不管就扔进垃圾箱里。

Paragraph 22: They don't exactly get put in the data. Yeah, they put them in the database.
他们并没有被准确地记录在数据中。对，他们被放入数据库中。

Paragraph 23: They put them in the dumpster.
他们把它们扔进了垃圾箱。

Paragraph 24: It's like the end of Indiana Jones and Rachel are stocks of those into some like their house.
这就像是《辛普森一家》中的结尾，那个家庭股票投资不成功，他们的房子也受到了影响。

Paragraph 25: So the top man are working on it exactly.
所以高层人员正在全力以赴地处理此事。

Paragraph 26: You know, it's like it's in some warehouse and it's never used again.
你知道，就好像它被存放在某个仓库里，再也没有被使用过了。

Paragraph 27: Oh, and I know hold on a second.
哦，我知道，等一下。

Paragraph 28: Let's pause for a second.
让我们停一下。

Paragraph 29: If this is paid for by the government in a lot of cases, the government owns it.
如果这些都是由政府支付的，在许多情况下，政府就是所有者。

Paragraph 30: So yeah, there's a rule that you have to make the data public two years after your clinical trial has been completed if it was funded by the government.
所以，如果你的临床试验是由政府资助的，完成后两年内你必须公开数据。

Paragraph 31: So that is sitting on a server somewhere or is it a website?
这是放在某个服务器上还是在一个网站上呢？意思是询问某个东西的存储位置，是否在某个服务器上或者在一个网站上。

Paragraph 32: So like government server where like it exists, that's like all put together.
这句话的意思是，就像政府服务器一样，所有的东西都集中在一起。

Paragraph 33: So like we have a group of people.
所以，我们有一群人。

Paragraph 34: So we aggregate data from lots of sources to train from and we love clinical trial data because it's this high-quality, amazing data sets.
因此，我们从许多来源聚合数据来进行训练，我们喜欢临床试验数据，因为这是高质量的、令人惊叹的数据集。

Paragraph 35: So like we have a group of people who like call up professors at universities and are like, Hey, you ran this clinical trial two years ago.
我们有一群人会给大学里的教授打电话，问他们：嘿，两年前你们做了这个临床试验。

Paragraph 36: So the day must be public now.
因此，这一天现在必须是公开的。 这句话的意思可能会根据上下文而有所不同，但通常指之前的某个活动、事件、计划等需要公开进行，可能涉及到公众或其他人的参与。

Paragraph 37: And then we aggregate the data.
然后我们汇总数据。

Paragraph 38: Yeah. It's pretty much like a freedom of information act.
是的，这差不多就像信息自由法案。

Paragraph 39: People will do in journalism, this freedom of information act to, Hey, listen, the government arrested this person.
人们会在新闻界中运用信息自由法，表达出“嘿，听着，政府逮捕了某人”的言论。

Paragraph 40: There are documents available, JFK assassination.
有一些文件可以查看，关于肯尼迪遇刺事件的。

Paragraph 41: You know, give us the information.
你知道的，把信息提供给我们。

Paragraph 42: The government will release some percentage of it or whatever.
政府会释放一定比例的它（指某个东西）。

Paragraph 43: You can actually start going and getting this information.
你可以开始获取这些信息了。

Paragraph 44: Yes.
是的。

Paragraph 45: And then putting it into AI models, this is something that we should have a Manhattan project on where some organization is paid like yours or another.
然后将其输入到人工智能模型中，这是我们应该联合展开一次"曼哈顿计划"的事情，其中一些组织像贵方或其他组织一样被支付。该计划旨在致力于开发先进的人工智能技术。

Paragraph 46: It could be private sector, public sector collaboration to make a database of anonymized data of every clinical trial that's gone on.
这可以是私营部门和公共部门合作，建立一个匿名数据库，记录所有进行的临床试验。

Paragraph 47: Then you could just set the AI on it.
然后你可以把人工智能应用到它上面。这样做可以让处理变得更简单。

Paragraph 48: 100% 100% agree.
完全完全同意。

Paragraph 49: Yeah.
是的。

Paragraph 50: And right now the other part of it is the industry sponsored trials.
眼下，另一方面是由行业赞助的试验。这种试验的意思是，由公司资助来评估他们自己的药物或者产品的有效性和安全性。

Paragraph 51: Like so there are pharma companies who are running all of these trials.
就像这样，有一些制药公司正在进行所有这些试验。这些试验指的是医药公司在测试新药物的安全性和有效性。

Paragraph 52: They own the data from those trials.
他们拥有那些试验的数据。

Paragraph 53: So that's typically how they make sense.
这通常是他们理解意义的方式。

Paragraph 54: They pay it for it. Yeah.
他们为此付出了代价。是的。

Paragraph 55: One could argue that maybe the patients should own their own data potentially.
有人可能会认为，患者可能应该拥有自己的数据。

Paragraph 56: But individually they should.
但是个体来说，他们应该这样做。

Paragraph 57: Yeah.
是的。

Paragraph 58: They don't.
他们不这样做。

Paragraph 59: But that's another point.
但那是另一个问题。

Paragraph 60: They really don't.
他们真的不这样认为。

Paragraph 61: They don't own a dual license to it.
他们没有这方面的双重许可证。这意味着他们不能完全拥有它或随意使用它。

Paragraph 62: No, many cases they're not giving it at all.
在许多情况下，他们根本没有提供它。这意味着他们没有提供所需的东西。

Paragraph 63: Yeah.
是的。

Paragraph 64: See that's something that some patients never find out whether or not they got the placebo or the real drug.
有些患者永远不会知道他们是否服用了安慰剂或真正的药物。

Paragraph 65: Okay.
好的。

Paragraph 66: So this is somewhere where like our government's not going to get this done because they're bought and paid for by pharma.
所以这里的情况是我们政府不会完成这个任务，因为他们已经被药企买通了。

Paragraph 67: I said that not you.
我说的不是你。

Paragraph 68: But this is something where the EU could pass something where they just say, listen, you're data, you get a copy of it, you get to know that.
但这是欧盟可以推出的一项政策，他们可以说，听着，你们的数据，你们可以获得一份副本，你们可以了解它。

Paragraph 69: Yeah.
是的。

Paragraph 70: Well, I mean, or the regulators, right?
嗯，我的意思是，或者说监管机构，对吧？

Paragraph 71: The FDA could say that if you want to submit your drug, you have to also submit an randomized data.
美国食品药品监督管理局可以说，如果您想提交药品，您还必须提交一份随机数据。这意味着药品测试所需的数据必须是随机化的，以确保药品的效果可以经过科学的评估和比较。

Paragraph 72: It's going to go into a database, right?
它将被放入数据库中，对吧？这句话在询问是否将信息存储在数据库中。

Paragraph 73: Yeah.
是的。

Paragraph 1: There's a huge amount of opportunity there because there's data from right now every year about 1 million patients participate in clinical trials across the board. So if you think about every year, there's 1 million people participating in that level of experiments and the data are not really being collected. So but that's where what we focus on is learning from those style of data.
第一段： 这里有巨大的机会，因为每年都有大约100万患者参加临床试验，所以有最新的数据。如果你想到每年都有100万人参加这种实验，但数据实际上没有被收集，所以我们专注于从这样的数据中学习。

Paragraph 2: In a country where you have socialized medicine like Canada, let's say the Canadian government with a with a pen stroke, Justin Trudeau, and it could just say, all this is put into an anonymized database, all the data, or just give people a choice. Hey, if you want to get free healthcare, you have to give away some amount of data to the collective good anonymized, or it could be opt-in. But I think if you're giving socialized medicine, it's not too much to ask that you're a blood results which the government paid for, get to be put anonymously, your name, your approximate region, maybe ethnicity, DNA, whatever.
在像加拿大这样有社会化医疗系统的国家里，假设加拿大政府可以用笔划的轻松一笔，让所有数据放入匿名数据库，或者给人们一个选择。比如，“如果你想获得免费医疗保健，你必须将一些数据为了改善社会整体情况交出来，或者以自愿方式选择。”但我认为，如果你获得了社会化医疗保健，要求你的血液测试结果（政府支付的）被匿名地加入到数据库中，包括你的姓名、大致地区和种族、DNA等，并不过分。

Paragraph 3: If you, or some amount of it gets, I got to think this through because it could get a little dystopian. That's complicated. It does get complicated to the state owns your data. But there is some trade of services here. So in a commercial country like the United States, it could be, will discount your rate if you put it into this pool for future research or like organ-doning, you could just do it out of the goodness of your heart. And a socialized medicine, they could say, listen, we just want everybody in the country to give their blood data to this research. I mean, there could be a way to do it in a very positive way. Is anything like that even being considered these days or no?
如果你的数据，或一部分数据被收集，我必须好好考虑一下，因为这可能会变得有点反乌托邦。这很复杂，因为如果国家拥有你的数据，那就会变得复杂起来。但是这里存在一些服务的交换。在像美国这样的商业国家中，如果你将数据放入未来研究或器官捐献的池中，可能会享受优惠费率，你也可以出于善心而这样做。在社会化医疗制度下，他们可能会说，听着，我们希望全国每个人都将他们的血液数据提供给这项研究。我的意思是，可能有一种非常积极的方式来做到这一点。这些天有没有考虑过这样的事情？

Paragraph 4: No. Why is it so obvious to us and not everybody else? Yeah, I mean, it's, yeah, it's difficult. Yeah, it's difficult. I think that there are a handful of, definitely a handful of countries that have better medical records where, you know, if you have a national health system, it's easier to have a national medical record system. But it's still not the case that these are the, these are like a repository of people taking part in these kind of research studies where you have a really rich, much more rich information about those people than you do a normal, like just from your medical records.
为什么这对我们来说如此明显，而不是每个人都能看到呢？我是说，这很困难。我想有一些国家会有更好的医疗记录，如果你有一个国民医疗系统，拥有一个国家医疗记录系统就更容易了。但这仍然不是所有人都参与这些类型的研究的储存库，你可以获得比医疗记录中普通信息更丰富的人们信息。

Paragraph 5: All right, everybody. Our friends from Microsoft are here. Tom Davis, a senior director at Microsoft for startups. How long has Microsoft been working on this cloud that you've now sort of uncovered and offered two founders? It's been years in the making, so to speak. The evolution of AI has taken many twists and turns in its journey at these large language models that have really been the game changer. And that's really thanks to OpenAI and the work that they've done and it's obviously our partnership.
大家好。我们的微软朋友们来了。Tom Davis是微软初创企业的高级总监。微软为这个云计算一直在努力多长时间？它经过了多年的发展，AI的演化在其历程中经历了许多曲折和转变，而那些大型语言模型确实是游戏规则改变者。这要归功于OpenAI及其工作以及我们的合作。

Paragraph 6: There has helped us really get ahead of the game on this. And we're seeing great companies like Peplexity.ai. In six months, they've built out an application that has now got millions of users. That wouldn't have been possible in a more traditional way of working. So it's great to see the innovation that startups are able to bring to the table now and not have to make these huge investments in time, resources and basically cash as well, which is always a premium when you're starting off your own company.
这帮助我们真正领先于这个领域。我们看到了像Peplexity.ai这样的伟大公司。在六个月内，他们开发了一款现在拥有数百万用户的应用。这在传统的工作方式下是不可能的。所以能够看到创业公司带来的创新，而不必在时间、资源和现金上进行巨额投资，这是非常好的。当你开始自己的公司时，现金始终是最重要的。

Paragraph 7: The Founder's Hub that Microsoft provides offers $150,000 in Azure cloud credits, all the development tools like GitHub and Teams, Office, all that great stuff. You get all that for free. Five minutes to sign up. Six figures and benefits. AKA dot ms slash this week and startups.
微软提供的"创始人中心"可以提供总价值15万美元的Azure云积分，还有所有的开发工具如GitHub、Teams和Office等，它们全部都是免费的。只需要花费5分钟注册即可获得六位数字级别的好处。网址是：AKA点ms斜杠本周和创业企业。

Paragraph 8: Thanks so much. Tom, tell me how does your company make money? Because you are startup. You raise a series of B.I. Understand you've done pretty well for yourself here. FEC's replacing a big bet on you. What's the business model?
非常感谢。汤姆，请告诉我你们公司是如何赚钱的？因为你们是初创企业。我知道你们已经呈现出相当不错的表现。FEC正在对你们进行重大投资。你们的商业模式是什么？

Paragraph 9: So we actually have a relatively simple to understand business model. Our value proposition for a a farmer company is that by working with us, your trial can be months, months shorter, many months. So it depends a little bit. Let's say six to months to a year shorter. And if your farmer company, your patent clock actually starts when you start your clinical trials. So every six to months shorter or a year shorter, that's an extra six months to a year of on patent sales. So it's billions of dollars in revenue for the farmer company. If the drug's successful.
我们实际上有一个相对简单易懂的商业模式。我们针对农民公司的价值主张是，与我们合作，您的试验可以缩短数月，甚至多个月。具体时间因情况而异，大约可以缩短6到12个月。如果您是农民公司，那么您的专利有效期实际上是从开始临床试验时开始计算的。因此，每缩短6个月或一年，就可以多获得6个月到一年的专利销售收入。如果这种药物取得成功，那么这还会带来数十亿美元的收入给农民公司。

And as we said, that's like one intent. So how do we do that? Well, the way we're going to do it is we're going to allow these farmer companies to run clinical trials that have smaller control groups than normal trials.
正如我们所说的那样，这就像一个意图。那么我们该怎么做呢？我们将允许这些农业公司开展临床试验，这些试验的对照组要比正常的试验小。

So you have fewer patients that you need for your trial. So let's say you need 100 fewer patients in your clinical trial. Well, there's a couple of things. One is that, you know, that again, it might take six months to find 100 patients who are willing to participate in your clinical trial.
你的试验需要更少的患者。假设你需要比原来减少100个患者参加临床试验。有几个问题需要考虑。首先是，你可能需要六个月的时间才能找到100个愿意参加临床试验的患者。

So right there, you've saved a whole bunch of time. That farmer company's also pay about $100,000 per patient in their clinical trials. Yeah. Yeah. So that's where that 100 million dollar number comes from. You get a thousand people in a trial. You're at 100 million. Yep. Yeah.
所以，你节省了很多时间。那家农业公司每名病人的临床试验费用约为10万美元。是的。是的。这就是那1亿美元的数字来源。如果你在试验中有1000人参加，你就需要1亿美元。是的。是的。

Exactly. And the patents are, I think it's pretty standard, 20 years. 20 years. So you're talking about a couple of year trial. What did you say? Five years. Five years. Five years. So you're at 15.
没错，专利的保护期一般为20年。这意味着你所说的几年试验只是一个标准的时间。你说是五年，那就是五年，这就意味着还剩下15年。

If you were to get them that extra year. Well, that's just one trial. You still have your phase one. You have your phase two. You've got your trial. So what is it by the time you get to work at how many years you got left on the pen? Probably like 10 or 12.
如果你能为他们争取到那个额外的一年。那只是一个试验。你还有你的第一阶段，第二阶段和试验。因此，当你开始工作时，钢笔上还有多少年可用？可能是10到12年左右。

So you have 10. If you save them one year, you get 10% more money. Yeah. That's right. Or 10% more time to exploit the drug. Exactly. Yeah. And not only, I mean, you know, that's the capitalist way. We can also frame it and say, well, there's a whole group of patients during that year. Who needed a drug? Who now get access to it?
所以你有10个。如果你存下来一年，你可以得到10%更多的钱。没错。就是这样。或者有10%更多的时间来商业化这种药物。没错。而且不仅如此，我是说，你知道，那是资本主义的方式。我们还可以把它说成，那一年里有一整群病人需要这种药物，现在他们可以获得这种药物的使用权了。

Because otherwise, if it was you wait another year, say you have, you know, groups of patients in a disease where people are dying. Right? If that drug's not available, all of those people are dead. So I know people with cystic fibrosis in the area. This is an area where it's particularly acute and they've made incredible progress. Yeah. But they're strong. Yeah. Extraordinarily expensive for a very small number of people. And there is some compassionate use of this, but it is really a challenging dynamic.
否则，如果你再等一年，假设你有一群患有病症的患者，这种疾病会导致人们死亡。对吧？如果这种药物不可用，所有这些人都会死去。所以，我知道该地区的囊性纤维化患者。这是一个尤其严重的地区，他们取得了惊人的进展。是的。但他们很坚强。对于少数人而言，这是极其昂贵的。有一些同情使用，但这确实是一个具有挑战性的动态。

Maybe you could talk about the long tail of diseases and how they should apply. Because that does also seem to be something unique. We have the, we have the me, the big four horsemen, you know, of, you know, diabetes and cancer and whatnot that kill people Alzheimer's, I think is in that group. But this, there's the long tail.
也许你可以谈谈疾病的长尾以及如何应用它。因为这似乎也是独特的。我们有著名的四大疾病杀手，如糖尿病、癌症等，以及阿尔茨海默症。但这里还有一个长尾的疾病群体。

So does this get a dramatic effect on the long tail as well? We think that this should be used in every single clinical trial period. Okay. Yeah. There are challenges that I would call technical and data challenges to getting there for all of these small diseases that also means there's a small amount of data to learn from. Right? So if we're talking about Alzheimer's, so many people have Alzheimer's, we can be really, really big data sets. But you start talking about, I don't know, something like cystic fibrosis is a lot smaller population.
那么，这会对长尾部分产生戏剧性影响吗？我们认为这应该在每个临床试验期间使用。好的。是的。对于所有这些小疾病，存在技术和数据方面的挑战，这也意味着要学习的数据量很小。是吧？如果我们在谈论阿尔茨海默病，那么有很多人患有阿尔茨海默病，所以我们可以有非常庞大的数据集。但是如果我们开始谈论像囊性纤维化这样的东西，那么人口就要小得多。

And so we still need to have enough data to train a machine learning algorithm. But we are working on that all the time. How we can do better with these small populations. And over the next few years, we want to be able to roll things out across everything. Actually, our ultimate goal right now, we basically, the way we do our machine learning is there will be one model per disease. So we have like a model for Alzheimer's. We have a model for ALS. We have a model for multiple sclerosis like that. We want to build one model for everything.
因此，我们仍需要有足够的数据来训练机器学习算法。但我们一直在努力改进。我们如何更好地处理这些小的数据集。在未来几年中，我们希望能够在所有领域都推出相关应用。实际上，我们现在的最终目标是，我们的机器学习方法将有一个疾病对应的模型。比如我们有一个阿尔茨海默病的模型。我们有一个ALS的模型。我们有一个多发性硬化症的模型以此类推。我们的目标是建立一个包括所有疾病的模型。

One model for like all human health. That's an extraordinary mission. I mean, this is very hard, but it's kind of like AGI versus vertical, right? Like you're, it is. It is. Verticalized cholesterol or heart disease, you know, you need a certain data set for that. But Alzheimer's might be overlap 50%, but not 100% in my ballpark correct here. That's the issue.
为所有人类健康制定一个模型，这是一个非凡的使命。我的意思是，这非常困难，但这有点像通用人工智能和垂直人工智能的比较，对吧？比如，对于垂直化的胆固醇或心脏病，你需要一定的数据集。但对于阿尔茨海默症来说，可能有50%的重叠，但并非100%。这就是问题所在。

Exactly. You know, that's right now. Yeah, so you get these specialized data sets. We build specialized models. But the whole point of it is, I think that in order for us to get into these smaller disease areas, we want to have something that looks sort of like a foundation model for health.
没错，你知道的，那就是现在的情况。是的，我们获得了这些专业的数据集，我们建立了专业的模型。但整个目的是为了让我们能够进入这些更小的疾病领域，我们希望拥有类似于健康基础模型的东西。

So one of the things we talk about these large foundation models today doing is that they can do either zero shot or few shot learning. And what that means is that they can learn to predict things having seen one example. So instead of having to give it like, we need just million examples for you to figure it out. Here's one example. What would happen in these next few examples? And so we want to probably be able to build something similar where for patients, even with really rare diseases, you can still figure it out from just a couple of examples.
我们今天谈论的较大基础模型的一个重要特点是可以进行零样本或少样本学习。这意味着它们可以仅凭一个样本就学会预测事物。因此，您不必提供很多样本来让它“学会”，只需要给出一个样本，它就能预测接下来的几个样本会怎样。因此，我们想要构建一个类似的系统，在这个系统中，即使对于患有非常罕见疾病的患者，我们仍可以从仅凭几个样本来诊断疾病。

That's extraordinary. In a way, it's like the foundational models of chat GPT or stable diffusion and some dolly and all this stuff. My understanding is people think you're going to be able to fit this on your smartphone. And so the model will eventually be on a chip.
这太神奇了。从某种意义上来说，这就像聊天GPT或稳定扩散模型以及一些机器人等基础模型。我的理解是人们认为你最终将能够将其适配到你的智能手机上。因此，这个模型最终将被放置在芯片上。

And when that happens, that's going to be pretty wild that you're just like you have a Wi-Fi chip or a graphics chip on your phone or computer, the concept of having an AI chip on there. That just yes is the next word in a sentence. And it's kind of starting you on third base every time you could do that for health.
当这种情况发生时，拥有一个AI芯片就像你的手机或电脑上的Wi-Fi芯片或图形芯片一样，这将会非常惊人。这意味着你每次都可以为健康领域打好基础。"yes"只是这句话中的下一个词。

Then my watch, my Apple watch might have this built into it. And it'd be like, wow, we're seeing something with your heart. We know it you ate. We have your blood sugar level because you have a container glucose monitor. It could be like doing stuff in real time. Oh, yeah. You could be doing real time interventions. Yeah, there's all kinds of stuff that are really interesting. And again, hard problems, but I want to know not just what is happening with me today, but what will happen with me in the future.
我的苹果手表可能会内置这些功能。它会显示我的心脏情况，了解我吃的东西，还有我的血糖水平，因为我配有葡萄糖监测器。这些功能会实时运行，可以进行实时干预。这些都是非常有趣的问题，虽然难度很大，但是不仅想知道今天的情况，也想了解未来会发生什么。

I want to have a something that can predict the state of my health in the future, depending on what I do today. If I change my diet in this way, how will that actually really affect my state of health over time? If I take on this different workout plan, how will that affect my state of health over time? And it's a super duper, duper hard problem. Right? You talked about how complex like human violence, human violence.
我想要一个东西，可以根据我今天的行为预测我未来的健康状态。如果我按照这种方式改变我的饮食，这实际上将如何影响我的健康状态？如果我采用这种不同的锻炼计划，这将如何影响我的健康状态？这是一个非常、非常难的问题。对吧？你谈到了类似人类暴力、人类暴力这样复杂的问题。

If human body has 37 trillion cells in it, it's actually 100 times the number of stars there are in the galaxy. So it's like a really, really complicated system. It's actually so complicated. I think that AI is going to be the only way we can tackle it. Right? It's too complicated for us to try to build up piece by piece.
如果人体内含有37000亿个细胞，实际上比银河系中的星星数量多100倍。它就像一个非常非常复杂的系统。它实际上是如此复杂，我认为人工智能将是我们处理它的唯一方法。对吧？这对我们来说太复杂了，我们无法一步一步地建立它。

So I think that AI is really going to be fundamentally the new language of biology in the end. We are going to describe biology in 10 or 20 years entirely in terms of like AI algorithms that are learned to understand and tame this complexity. And once you get to that point, yeah, the idea of you have your own digital twin that's on your computer that talks about your health and maybe your doctor also has that same thing.
我认为，人工智能最终将成为生物学的新语言根基。未来10到20年，我们将会完全以人工智能算法描述生物学并学会理解并驾驭其复杂性。一旦达到这一点，就会出现你自己的数字孪生体，它在你的电脑上提供有关你的健康状况的信息，而你的医生也可能拥有同样的信息。

You don't even need to go to the doctor's office anymore. They just pull up your digital twin and they can see what is happening with you today and what's going to happen with you in the future and they can design treatment plants. Maybe it's even an AI doctor, but I think that's the future. You already have this happening again, back to a vertical AI versus general AI, which is analogous to what we're talking about here.
现在你甚至不需要去医生办公室了。他们只需查看你的数字孪生，就能知道今天和未来会发生什么，并设计治疗方案。也许这还是一位AI医生，但我认为这是未来。你已经再次经历了这种情况，回归到竖直AI与通用AI的比较，这与我们在这里谈论的相似。

You already have in X-rays, people are starting to build technology to look at the X-rays or to look at heart rate monitors over time and just highlight stuff that then goes to a doctor and that's augmentation. So what I've been really thinking about in this AI future because this is moving rapidly.
在X射线技术已经存在的情况下，人们开始研发技术来观察X光或是心率监测器的数据变化，并突出那些需要医生关注的内容，这就是增强现实技术。因此，我一直在思考，在这个人工智能未来不断快速发展的时代，我们该如何应对。

You've been doing this for six years. How would you describe the pace we've seen of the past year compared to the decade before? It's definitely moving faster. What's interesting in, I think what's happened more is that we finally reached a threshold of utility.
你已经做了六年了。与过去十年相比，你认为我们见证了过去一年的发展速度如何？它肯定更快。我认为更有趣的是，我们终于达到了实用的门槛。

Things seem like they are moving really fast when you're near a threshold of utility, even if they're moving slow. Because if you just stay at a linear line, you just increase by X a little bit every year, but there's some threshold that would you need to pass before people care about it. You will seem like no progress has happened and all of a sudden you'll pass that threshold everywhere like, wow, amazing progress.
当你接近实用阈值时，事情似乎移动得非常快，即使它们移动得很慢。因为如果你只停留在线性线上，你每年只会稍微增加一点X，但有一些阈值你需要超越，这才是人们关心的。你会感觉好像没有进展，突然你就会在所有地方通过那个阈值，看到令人惊叹的进展。

I do kind of think that's where we're at that the AI research has been pretty steady progress over the past 10 or 15 years to bring us to this point. But it's all of the sudden got good enough that we're willing to use it. If you think about GPT-3, the API to that was released three years ago. It's not like we're in some exponential speed up of Terminator world because that was a three years waiting period between three and four.
我有点觉得我们现在正处于这种情况——在过去的10或15年中，人工智能研究有了相当稳定的进展，把我们带到了这一点。但突然间，它变得足够好，我们愿意使用它了。如果你想想 GPT-3，其API是在三年前发布的。这并不意味着我们现在正处于某种指数级的《终结者》式急剧加速世界，因为那是在三年到四年之间的等待期。

That's actually not that fast. It's more that what's happened is people have figured out, oh, while these models are actually able to solve stuff now, we can build applications on them. Now it feels like it's incredibly fast because there's all these applications because before they weren't useful, and now they are. And so it's really more of a cross that threshold of utility than I think a real speed up in the research.
其实，它并不是那么快。更确切地说，人们已经意识到，这些模型现在可以解决许多问题，我们可以在其上构建应用程序。现在感觉非常快，是因为有很多应用程序可用了，以前它们没有用处，现在有了用处。所以，我认为这更多是通过实用性的门槛，而不是真正的加速研究。

The research is kind of the same. Yeah. There is something perhaps to humans using it, finding the utility and then the reinforcement learning or these GPT-starding different language models, different AI instances learning from each other. That also is, once you get humans using it, it's like, well, GPS is really interesting for sending a missile or tracking a plane, but it's also pretty good at finding a bakery or getting an Uber, right?
这项研究还是一样的。是的，或许对于人类来说会有一些用处，人们会发现其实用途，然后再进行强化学习或使用GPT等不同语言模型，不同的AI实例进行相互学习。一旦人们开始使用它，就像GPS一样，它不仅很有趣，可以用于发射导弹或跟踪飞机，还可以很好地帮助人们找到面包店或叫一辆Uber车，对吧?

It's like the GP, the street finds it's used for technologies that William Gibson said. And it's like, that really feels like what's happening once you put language models into a chat format. It's just, or you start building auto-GTPs or plugins. It's like with streets figuring out all kinds of interesting use cases for it.
这就像是 GP（一种电子技术设备），街道发现它的用途就像 William Gibson 所说的一样。而且当你把语言模型应用到聊天界面中时，真的感觉到这就是正在发生的事情。就像您开始构建自动 GTP 或插件一样，街道正在发现各种有趣的使用案例。

Hey, listen, thanks for doing this work. And on the revenue question, since you saved them that extra year, you just want to take a percentage of that or take a percentage of how much less people can be in the study. Is that the ultimate goal?
嘿，听着，感谢你做这份工作。关于收入问题，由于你为他们节省了一年的时间，你只是想从中获取一定比例的收益，或者从研究中减少了多少人的比例来获取。这是最终目标吗？

Yeah. So that's why I was saying earlier, the business model is relatively simple. So if you're paying $100,000 per patient and we remove one patient, you should pay us $100,000 if you remove two, $200,000. So we just get paid based on how much smaller we can make your clinical trials. That's our main business model. Maybe a split of 50-50, so they get a little savings. Yeah, we try to take them.
是的，这也是我之前说的，这家公司的商业模式相对较简单。比如说，如果你为每位患者支付100,000美元，如果我们可以去除一个患者，你应该支付给我们100,000美元，如果我们可以去除两个患者，你就需要支付200,000美元。所以我们的主要商业模式就是根据我们能让你的临床试验变得更小，我们就可以得到相应报酬。也许可以一拍即合，分成50-50，让他们也能够节省少许费用。是的，我们会尽力为他们争取到更多的节省。

Again, they're getting billions of dollars in saying additional sales. So they're getting a great deal by working with us. It's an example of our work in clinical trials, I think, is a really unusual example of something that everybody wins from because the pharma company, they definitely benefit. They can make a huge amount of additional sales. But the patients also very clearly benefit because you have a smaller control group and you have a faster time to market for the drug. Even the regulators and people benefit from this because it's a use of AI. Well, actually, we can prove that the clinical trials produce the same rigorous, scientifically rigorous results. So everybody benefits from this technology. I think that there's going to be a lot more areas in health where this is the case, where a technology is just going to totally benefit everybody.
他们现在通过增加销售额获得数十亿美元的收入。因此与我们合作对他们来说非常有利。我认为，这是我们在临床试验方面的工作的一个非常不寻常的例子，因为所有人都从中受益，制药公司肯定会获益，因为他们可以增加大量销售额，而患者也能从中获得明显的好处，因为控制组更小，药物的上市时间更快。即使监管机构和相关人员也因此受益，因为这是使用人工智能的一种方式。我们可以证明，临床试验可以产生同样严谨、科学可靠的结果。因此，每个人都从这项技术中获得了好处。我认为，在医疗领域，还有很多这样的领域，采用这种技术将完全使每个人受益。

I think the difficult part of building in this space is that it's this legacy conservative industry and trying to figure out how to get people to trust and adopt new technologies as hard.
我认为在这个领域建设的难点是它是一种传统保守的行业，而要想办法让人们信任和采用新技术是很困难的。

Yeah, we have the Wikipedia as an example of a foundational data set and the DBPedia that's being kind of built off of it, really helped train these models. Is there an equivalent in your world? And if not, would that not be something noble for the government to work on if way of saying, hey, let's find 10,000 people in the United States and give them a battery of tests for their lives and really get that data set and open source it to the world to learn from?
是啊，我们有维基百科作为基础数据集的例子，而DBPedia是基于它建立的，它真的有助于训练这些模型。你的领域有类似的东西吗？如果没有，政府是否应该致力于这个事业呢？比如说，让我们在美国找到10,000人，对他们进行一系列的生活测试，得出这个数据集并开源给全世界学习。

I don't think that there's one, there have been attempts to kind of go in that direction. The UK Biobank is an example of something that kind of starts to look a little bit more like this, which is the NHS version of this. So exactly that NHS is like, hey, we have a national health system. We could create a big open source data set for everybody and there is a big open source data set.
我不认为这种数据集已经存在，但已经有人尝试朝这个方向发展。英国生物库（UK Biobank）就是一个开始朝这个方向发展的例子，这是英国国民保健制度（NHS）的一个版本。实际上，NHS就是说，“我们有一个国民保健系统，我们可以为所有人创建一个大型开源数据集”，确实存在一个大型开源数据集。

Verily, also in Google had tried to run something they called project baseline. I don't know what its current status is, but the whole idea was enroll 10,000 people into a big observational study and follow them for a bunch of years and collect all this information and then we'd have this data set to learn from.
实际上，谷歌也曾尝试运行一个称为“基准项目”的东西。我不知道它现在的状况是什么，但整个想法是招募10,000人参与一项大型观察性研究，并跟踪他们多年并收集所有这些信息，然后我们将有这个数据集来学习。

That was Verily, right?
那是Verily，对吧？

Verily, yeah, yeah, yeah. Exactly. That was Google's live forever health care thing. Well, I think you need something to learn from, right? Like kind of what you're saying. So baseline is like, hey, let's collect this data set. Let's build this thing that we could learn from. Again, I don't know what the status of that is.
确实，对的，没错。那就是谷歌的长生不老健康项目。嗯，我想你需要一些学习的东西，对吧？就像你说的那样。所以基线就是，嘿，让我们收集这个数据集。让我们建立这个我们可以学习的东西。我不知道现在的状况如何。

So there have been a few different options, but I think that I 100% would support the US government trying to build a similar type of data set. Yeah, the ability to reduce suffering, extend health span.
有几种不同的选择，但我认为我百分之百支持美国政府尝试建立类似类型的数据集。是的，能够减少痛苦，延长健康寿命。

Maybe we don't add years to life as Peter and his new book has been talking about Tela, I guess this is any pronounciation last name. He talks a little bit about health span versus lifespan. Hey, you live the same number of years, but you're skiing in your 70s and 80s. Or riding bikes in your 90s. It feels like we're on the cusp of something very interesting here.
也许我们并不像彼得和他的新书所讲的那样让生命延长岁月，我猜这是一个人的姓氏“Tela”。他谈了一点关于健康寿命和生命寿命的区别。嘿，你活了同样的年数，但你在70岁和80岁时滑雪，或者在90岁时骑自行车。感觉我们正处于一些非常有趣的东西的前沿。

And so just a behalf of humanity. Thank you for choosing this for your entrepreneurial journey and that you're doing God's work. Or if you're an atheist, you're doing humanity's work. So pick whichever you like. No judgements either way. Thanks for coming on the program and maybe we can catch up in a year and hear how you're doing next year with this.
所以这是人类的一小步。感谢你在创业之路上选择了这个，你在做上帝的工作。或者如果你是无神论者，你在做人类的工作。所以选你自己喜欢的那个吧，不管怎样都没有关系。感谢你参加这个节目，也许我们明年可以再见面，听听你对这件事的情况。

Yeah, sounds great. Thanks for having me.
哦，听起来很棒。谢谢邀请我。

All right, cheers. Come on on the program.
好的，谢谢。快来节目中吧。