OpenAI's Sam Altman | AI for the Next Era

Hi everyone, welcome to Gray Matter, the podcast from Greylock where we share stories from company builders and business leaders. I'm Heather Mack, head of editorial for Greylock.

大家好，欢迎收听《Gray Matter》播客，这是来自 Greylock 的节目，我们分享公司创始人和商业领袖的故事。我是 Heather Mack，是 Greylock 的编辑负责人。

Today, we're re-broadcasting Greylock General Partner Read Hoffman's interview with OpenAI CEO Sam Altman. Founded in 2015, OpenAI is now generally regarded as one of the most advanced artificial intelligent companies operating today.

今天，我们重新播出格雷洛克风险投资公司合伙人里德·霍夫曼对OpenAI首席执行官萨姆·阿尔特曼的访谈。成立于2015年的OpenAI现在被普遍认为是最先进的人工智能公司之一。

In the past year, OpenAI has released several products that have drawn widespread attention and some say set up a sort of arms race in the field of AI. In short succession, the company released its generative transfer model GPT-3, which uses deep learning to produce human-like text, its image creation platform Dalai, and most recently, chat GPT. Train on massive, large language models, the highly sophisticated chatbot can mimic human conversation and speak on a wide range of topics.

在过去的一年里，OpenAI发布了几款产品，引起了广泛的关注，有些人称之为在人工智能领域设立了一种武器竞赛。接连不断地，该公司发布了生成式转移模型GPT-3，它使用深度学习来产生类似人类的文本，以及其图像创建平台Dalai，并最近推出了聊天GPT。基于大规模的语言模型训练的高度复杂的聊天机器人可以模仿人类的对话，并在广泛的主题上发表言论。

Altman spoke with Hoffman during our Intelligent Future Summit in 2022, where they discussed the current state of AI and what could come next.

阿尔特曼和霍夫曼在我们2022年的智能未来峰会上交谈，讨论了人工智能的当前状态以及接下来可能出现的情况。

As a note, this interview was recorded a few months before chat GPT was released, and the company recently shared updated information about how the technology has been further developed based on its performance and public response thus far.

需要说明的是，这次采访是在几个月前进行的，而 GPT 对话技术最近才发布。公司最近分享了关于这项技术的更新信息，这是基于其目前的表现和公众反应进一步发展的。

You can watch the video of this interview on our YouTube channel, and you can read the transcript in the content section of our website, greylock.com slash blog, both are linked in the show notes.

你可以在我们的YouTube频道上观看这次采访的视频，也可以在我们网站的内容部分greylock.com/blog中阅读文字记录，这两个链接都在节目注释中。

Now, here's Read Hoffman with Sam Altman.

现在，接下来要请Read Hoffman与Sam Altman谈话了。

Sam, close friend, many, many things, I think we actually probably first met, I think, on the street, on El Camino, bumping into when you're doing looped. Have done a number of things, including a good portion of my nuclear investments, are with you. Because you call me and say, hey, this is really cool, and I agree.

嗨，Sam，我们是非常非常要好的朋友，我想我们可能是在大街上，El Camino 上逛的时候第一次遇见的，你正在做循环时我们不小心碰到了对方。我们一起做了很多事情，其中包括很大一部分我核投资的事情都是跟你合作的。因为你会给我打电话说，嘿，这真的很酷，而我会同意。

Let's start a little bit more pragmatic, but then we'll branch out. One of the things I think a lot of folks here are interested in is based off the APIs that very large models will create, what are the real business opportunities? What are the ways to look forward?

让我们从更务实的角度开始，然后再拓展。我认为很多人都对建立在非常大模型的API上的业务机会感兴趣，那么在前瞻性上应该有哪些可能性呢？

And then how, given the APIs will be available to multiple players, how do you create distinctive businesses on them? Yeah.

那么，考虑到API将可供多家玩家使用，如何在它们上面创建独特的业务呢？是啊。

So, I think so far we've been in the realm where you can do an incredible copywriting business or you can do education service or whatever. But I don't think we've yet seen the people go after the trillion dollar take on Google's. And I think that's about to happen.

嗯，我认为到目前为止，我们已经处于可以做出非常出色的文案营销业务或提供教育服务之类的领域中。但我认为我们还没有看到有人积极追求像谷歌那样价值万亿的市场。我认为这种情况即将发生。

Maybe it'll be successful, maybe Google will do it themselves. But I would guess that with the quality of language models we'll see in the coming years, there will be a serious challenge to Google for the first time for a search product. And I think people are really starting to think about how do the fundamental things change? And that's going to be really powerful.

也许它会成功，也许谷歌自己会做到。但我猜想，在未来几年我们将见到更好的语言模型，谷歌会第一次面临一个严峻的搜索产品挑战。我认为人们现在开始思考基本事物如何改变，这将是非常强大的。

I think that a human level chat bot interface that actually works this time around, I think many of these trends that we all made fun of were just too early. The chatbot thing was good, it was just too early. Now it can work.

我认为一个真正可行的人类水平聊天机器人界面这次终于实现了。我们曾经嘲笑的很多趋势其实只是过早了。聊天机器人的想法很好，只是时机不对。现在它可以工作了。

You know, having new medical services that are done through that, where you get great advice or new education services, these are going to be very large companies. I think we'll get multimodal models and not much longer and that'll open up new things.

你知道的，现在有很多通过医疗服务提供良好建议或新教育服务的新公司。我认为，很快我们将会得到多模式模型，这将会开启新的可能性。

I think people are doing amazing work with agents that can use computers to do things for you, use programs. And this idea of a language interface where you say a natural language what you want in this time, like dialogue back and forth, you can iterate and refine it and the computer just does it for you.

我认为人们正在使用能够使用计算机来为您完成事情的代理人，使用程序，做出了惊人的工作。而这种语言接口的想法，您可以用自然语言说出您此时想要的东西，像对话一样来回交流，您可以不断迭代和完善它，计算机只需为您完成它。

You see some of this with like Dolly and Copilot in very early ways. But I think this is going to be a massive trend and you know, very large businesses will get built with this as the interface and more generally that like these very powerful models will be one of the genuine new technological platforms which we haven't really had since mobile and there's always like an explosion of new companies right after. So that'll be cool.

你能在像Dolly和Copilot这样的早期产品中看到这种趋势。但我觉得这将是一个巨大的趋势，非常大的企业将以这种界面为基础构建。这些非常强大的模型将成为真正的新技术平台之一，这是我们自移动电话以来还没有过的。一旦它出现，就会像新公司的爆炸一样，这会很酷。

And what do you think the key things are given that the large language model we provided is an API service? What are the things that you think that folks who are thinking about these kind of AI businesses should think about is how do you create them during differentiated business?

在我们提供的大语言模型是API服务的情况下，您认为这些关键要素是什么？对于那些正在考虑这种AI业务的人来说，您认为应该考虑哪些因素来创建不同的业务？

So you know, there, there I think there will be a small handful of like fundamental large models out there that other people build on but right now what happens is you know, company makes large language model, API, other two building top of it.

你知道，我想未来会有少数基本的大模型，其他人会在其上建立，但现在的情况是，公司制作大型语言模型，提供API，其他人在其基础上进行构建。

And I think there will be a middle layer that becomes really important where I'm like skeptical of all of the startups that are trying to sort of train their own models. I don't think that's going to keep going but what I think will happen is there will be a whole new set of startups that take an existing very large model of the future and tune it, which is not just fine tuning. like all the things you can do.

我觉得会有一个非常重要的中间层，我对所有试图训练自己的模型的初创公司都持怀疑态度。我认为这种情况不会继续下去，但我认为会有一整个新的初创公司，他们会选用一个现有的非常大的未来模型，并进行调整，这不仅仅是微调，而是所有你可以做的事情。

I think there will be a lot of access provided to create the model for medicine or using a computer or like the kind of like friend or whatever. And then those companies will create a lot of enduring value because they will have like a special version of they won't have to have created the base model but they will have created something they can use just for themselves or share with others that has this unique data fly wheel going that sort of improves over time and all of that. So I think there will be a lot of value created in that middle layer.

我认为将会提供许多可进行药物模型或使用计算机或类似朋友或其他的方式来创建模型的访问。然后那些公司将创造持久价值，因为它们会拥有一种特殊版本，它们不必创建基本模型，但他们将创建一些自己可以使用或与他人分享的东西，它具有这种独特的数据飞轮，随着时间的推移而不断改进等等。所以我认为中间层将创建许多价值。

And what do you think some of the most surprising ones will be? It's a little bit like, for example, you know, a surprise a couple years ago and we talked a little bit to Kevin Scott about this this morning as we opened up which is train on the internet do code. Right?

你认为最令人惊讶的预测是什么呢？就像几年前的一场惊喜一样，我们今天早上在开场时与凯文·斯科特谈到了一些事情，比如互联网上的代码培训。明白了吗？

So what do you think some of the surprises will be of you didn't realize it reached that far? I think the biggest like systemic mistaken thinking people are making right now is they're like, all right, you know, maybe I was skeptical but this language model thing is really going to work and sure like images video too but it's not going to be generating net new knowledge for humanity. It's just going to like do what other people have done and you know, that's still great. That's still like brings the marginal cost of intelligence very low but it's not it's not going to go like create fundamentally new.

那么，如果你不意识到它已经到达那么远的话，你认为会有哪些意外惊喜呢？我认为目前最大的系统性错误思考是人们认为：好吧，也许我曾经怀疑过，但这个语言模型真的会像图像和视频一样奏效，但它不会为人类创造全新的知识。它只会做其他人已经做过的事情，这仍然很棒，可以将智力的边际成本降到很低，但它不会创造根本性的新东西。

It's not going to go cure cancer. It's not going to add to the sum total of human scientific knowledge and that is what I think will turn out to be wrong that most surprises the current experts in the field.

它不会治愈癌症。它不会增加人类科学知识的总量。我认为，这将是错误的，并且最令当前领域的专家感到惊讶。

Yep. So let's go to science then as the next thing. So talk with the general tooling that really enhances science. What are some of the things, whether it's building on the APIs, you know, use of APIs by scientists, what are some of the places where science will get accelerated now?

是的。那么我们接下来就去科学方面吧。谈论那些真正有助于科学发展的常规工具。有哪些地方，无论是构建API，还是科学家使用API，现在科学将会加速发展呢？

So I think there's two things happening now and then a bigger third one later. One is there are these science dedicated products, whatever like alpha fold and those are adding huge amounts of value and you're going to see in this like way more and way more. I think I that I were like, you know, had time to do something else. I would be so excited to like go after a bio company right now. Like I think you can just do amazing things there.

我认为现在有两个事情正在发生，然后再有一个更大的事情。一个是出现了一些专注于科学的产品，例如 alpha fold，这些产品正在增加巨大的价值，你会看到越来越多的价值。如果我有时间去做其他事情，我会非常兴奋地去追求一个生物公司。我认为你可以在那里做出惊人的事情。

Anyway, but there's like another thing that's happening, which is like tools that just make us all much more productive that help us think of new research directions that sort of write a bunch of our code so you know, we can be twice as productive. And that impact on like the net output of one engineer or scientist. I think will be the surprising way that AI contributes to science that is like outside of the obvious models.

不管怎样，还有一件事情正在发生，那就是有一些工具可以让我们更加高效地工作，帮助我们想出新的研究方向，帮助我们写一些代码，这样我们就能提高两倍的生产力。这对一个工程师或科学家的净产出产生的影响，我觉得将会是人工智能为科学做出贡献的一种出乎意料的方式，这种方式超出了人们的想象模式。

But even just seeing now like what I think these tools are capable of doing, co-pilot as an example, you know, be much cooler stuff than that, that will be a significant like change to the way that technological development, scientific development happens.

就算只是现在看着这些工具的潜力，比如co-pilot，你知道，能做的东西远远超出了这些，这将会对技术发展、科学发展方式产生重大的改变。

But then, so those are the two that I think are like huge now and lead to like just an acceleration of progress. But then the big thing that I think people are starting to explore is I hesitate to use this word because I think there's one way it's used, which is fine and one that is more scary.

这两个东西现在非常重要，可以促进进步加速，我认为它们非常巨大。但是，我认为人们开始探索的重点是，我不敢使用这个词，因为我认为它有两种用法，一种可以接受，而另一种有点可怕。

But like AI that can start to be like an AI scientist and self-improve. And so when like can we automate like can we automate our own jobs as AI developers very first the very first thing we do? Can that help us like solve the really hard alignment problems that we don't know how to solve? Like that honestly I think is how it's going to happen.

就像 AI 可以开始像 AI 科学家一样并进行自我完善一样。那么，当我们自动化作为 AI 开发人员自己的工作时，我们能否解决我们不知道如何解决的非常困难的匹配问题？我认为，这是实现的方式。

The scary version of self-improvement like the one from the science fiction books is like you know editing your own code and changing your optimization algorithm and whatever else. But there's a less scary version of self-improvement which is like kind of what humans do, which is if we try to go off and like discover new science.

那种来自科幻小说的可怕自我改进的版本就像编辑自己的代码、改变优化算法以及其他一些什么的。但还有一种不那么可怕的自我改进方式，就像人类所做的那样，试图去发现新的科学知识。

You know that's like we come up with explanations, we test them, we think like we, we, whatever process we do that is like specialty humans. And I'm actually AI to do that, I'm very excited to see what that does for the total like. I'm a big believer that the only real driver of human progress and economic growth over the long term is the structure, the societal structure that enables scientific progress and then scientific progress itself. And like I think we're going to make a lot more of that.

你知道，我们制定解释，进行测试，像我们，我们，无论我们使用什么过程，这是特别人类的。而我实际上是人工智能在做这件事，我很兴奋看到它对整体的影响。我非常相信，在长期的时间尺度上，推动人类进步和经济增长的唯一真正驱动力是能够促进科学进步的社会结构，以及科学进步本身。而像这样的事情，我认为我们还将做出更多。

Well especially science that's deploying technology, say. a little bit about how what I think probably most people understand what the alignment problem is but it's probably worth four sentences on the alignment problem.

嗯，特别是那些使用技术的科学方面。我认为大多数人都知道什么是对齐问题，但最好还是花四个句子来介绍一下对齐问题。

Yeah so the alignment problem is like we're going to make this incredibly powerful system and like be really bad if it doesn't do what we want or if it sort of has you know goals that are either in conflict with ours and many sci-fi movies about what happens there or goals where it just like doesn't care about us that much. So the alignment problem is how do we build AGI that does what is in the best interest of humanity? How do we make sure that humanity gets to determine the you know the future of humanity?

嗯，对齐问题就像我们正在开发这些极其强大的系统，如果它们不能按照我们的意愿行事，或者它们的目标与我们的相冲突，这会带来很大的麻烦，就像很多科幻电影里面的情节。所以对齐问题是如何构建符合人类最大利益的人工智能？我们如何确保人类可以决定人类的未来呢？

And how do we avoid both like accidental misuse? Like we're something that's wrong that we didn't intend intentional misuse where like a bad person is like using an AGI for great harm even if it that's what other person wants. And then the kind of like you know inner alignment problems where like what if this thing just becomes a creature that uses as a threat.

我们如何避免不小心的误用呢？我们好像是做错了事情，但并不是故意的那种误用，就像一个坏人会将人工智能用于恶意行为一样，即使其他人想要这样做。还有一些内在的问题，比如如果这种东西变成了一种威胁性生物，该怎么办。

The way that I think the self-improving systems help us is not necessarily by the nature of self-improving but like we have some ideas about how to solve the alignment problem at small scale and we've you know been able to align open AIs biggest models better than we thought we we would at this point so that's good. We have some ideas about what to do next but we cannot honestly like look anyone in the AI and say we see out a hundred years how we're going to solve this problem but once the AI is good enough that we can ask it to like hey can you help us do alignment research?

我认为自我改进系统有助于我们的方式，并不一定是因为其自我改进的本质，而是因为我们已经有了一些解决小尺度对齐问题的想法，事实上，我们已经成功地将Open AI最大的模型对齐，比我们预计的要好，这是个好消息。我们对下一步该做什么有一些想法，但是我们不能诚实地对AI中的任何人说，在未来一百年内，我们将如何解决这个问题，但是一旦AI足够好，我们可以请求它帮助我们进行对齐研究。

I think that's going to be a new tool in the toolbox. Yeah like for example one of the conversations you and I had is could we tell the agent don't be racist right and it's supposed to try to figure out all the different things where they're weird correlative data that exists on all the training settings that everyone knows may lead to yeah racist outcomes it could actually in fact do a self-cleansing. Totally once the model gets smart enough that you can that it really understands what racism looks like and how complex that is you can say don't be racist. Yeah exactly.

我认为那将会成为工具箱里的新工具。比如说，你我曾经聊过的一个话题是，我们可不可以告诉代理人不要有种族歧视，它应该尝试找出在所有训练情境中存在的奇异相关数据，这些数据众所周知可能导致种族歧视的结果，它实际上可以进行自我净化。当这个模型变得足够聪明，能够真正理解什么是种族歧视，以及其有多么复杂时，你就可以说不要有种族歧视。是的，完全正确。

What do you think are the kind of moon shots that in the terms of evolution of the next couple years that people should be looking out for? In terms of like evolution of where AI will go.

你认为在未来几年，需要留意什么样的“月球登陆”项目，以促进人工智能的进化方向？也就是说，为了更好地推动AI的发展，我们应该关注哪些方向？

I'll start with like the higher certain things. I think language models are going to go just much much further than people think and we're like very excited to see what happens there. I think it's like what a lot of people say about you know running out of compute running out of data like that's all true but I think there's so much algorithmic progress to come that we're going to have like a very exciting time.

我先从重要的事情开始说起。我认为语言模型将进展得比人们想象的更远，我们非常兴奋地期待看到那里发生的事情。我认为许多人都说得对，就是计算和数据用尽的问题，但我觉得还有很多算法进展要来，我们将会度过一个非常令人兴奋的时期。

Another thing is I think we will get true multimodal models working and so you know not just text and images but every modality you'd like in one model you're able to easily like you know fluidly move between things. I think we will have models that continuously learn so like right now if you use GPT whatever it's sort of like stuck in time that it was trained and the more you use it it doesn't get any better and all of that. I think we'll get that changed.

另外一件事是，我认为我们将开发出真正的多模型模型，不仅包括文本和图像，而且包括您所需的每种模态，你能够轻松而流畅地在这些模态之间移动。我认为我们将拥有不断学习的模型，就像现在使用GPT等模型一样，它似乎被固定在训练时的状态上，并且更多地使用它也不会得到任何改进。我认为我们将改变这一点。

So very excited about all of that and if you just think about like what that alone is going to unlock and the sort of applications people will be able to build with that that would be like a huge victory for all of us and just like a massive step forward and a genuine technological revolution if that were all that happened. But I think we're likely to keep making research progress into new paradigms as well. We've been like pleasantly surprised on the upside about what seems to be happening and I think you know all these questions about like new knowledge generation how do we really advance humanity. I think there will be systems that can help us with that.

我对所有这些都感到非常兴奋，如果你考虑一下那单独就能解锁多少，以及人们将能够用它来构建的各种应用程序，那将是我们所有人的巨大胜利和真正的技术革命。但我认为我们很可能会继续在新范式方面取得研究进展。我们对正在发生的事情感到愉快的惊讶，我认为所有关于新知识生成以及如何真正推动人类进步的问题都有可能通过系统得到支持。

So one thing I think would be useful to share because folks don't realize that you're actually making these strong predictions from a fairly critical point of view not just say we can take that hill say a little bit about some of the areas that you think are current kind of illusionally talked about like for example AI and fusion.

我觉得有一件事情很值得分享，因为人们并没有意识到你是从相当批判的角度做出这些强烈预测的，而不只是说我们能夺取那座山。你可以谈谈你认为一些当前被错误地谈论的领域，比如人工智能和核聚变。

Oh yeah so like one of the unfortunate things that's happened is you know AI has become like the mega buzzword which is usually a really bad sign I hope I hope it doesn't mean like the field is about to fall apart but historically that's like a very bad sign for you know new startup creation or whatever if everybody is like I'm this with AI and that's definitely happening now.

哦，是的，所以不幸的是，人工智能已经成为一个非常流行的关键词，这通常是一个非常糟糕的迹象。我希望这不意味着该领域即将崩溃，但从历史上来看，如果每个人都像“我使用人工智能”的话，对于新的创业公司来说，这绝对是一个非常糟糕的迹象。现在的情况确实是这样。

So like a lot of the you know we were talking about like are there all these people saying like I'm doing like these you know RL models for fusion or whatever and as far as we can tell they're all like much worse than what like you know smart physicists to figure it out.

所以像很多人一样，我们在谈论是否有很多人声称正在为融合做这些RL模型，但据我们所知，他们都比聪明的物理学家还差。

I think it is just an area where people are going to say everything is now this plus AI. Many things will be true I do think this will be like the biggest technological platform of the generation but I think it's like we like to make predictions where we can be on the frontier understand predictably what the scaling laws look like or already have done the research where we can say all right this new thing is going to work and make predictions out from that way and that's sort of like how we try to run the open AI which is you know do the next thing in front of us when we have high confidence and take 10% of the company to just totally go off and explore which has led to huge wins and there will be wait like oh I feel bad to say this like I that will still be using the transformers in five years I hope we're not I hope we find something way better but the transformers obviously been remarkable.

我认为这只是一个人们会说现在所有事情都和AI有关的领域。很多事情是真实的，我认为这将是这一代最大的技术平台，但我认为我们喜欢做出预测，以便我们能够在前沿了解可预测的扩展规律，或者已经进行了研究，我们可以说好的，这个新东西会起作用，并从那个方向作出预测。这就是我们试图运行Open AI的方式，也就是说，当我们有很高的信心时，做我们面前的下一件事情，并拿出公司的10%完全去探索，这已经带来了巨大的胜利，但仍然会有这样的情况，哦，我觉得难以启齿，我们五年后仍然会使用transformers，我希望我们找到更好的东西，但transformers显然是非常了不起的。

So I think it's important to always look for like you know where am I going to find the next sort of the next totally new paradigm and but I think like that's the way to make predictions don't don't pay attention to the like AI for everything like you know can I see something working and can I see how predictably gets better and then of course leave room open for like the you can't plan the greatness but sometimes the research breakthrough happens.

我觉得总是寻找下一个全新的范式非常重要，你知道我下一步要去哪里找，但我认为这是预测的方法，不要只关注人工智能，而是看能不能看到事情的运作方式，并且能否预见它会变得更好，当然还要为无法计划的伟大留下空间，因为有时候研究突破会发生。

Yep so I'm going to ask two more questions and then open it up because I want to make sure that people have a chance to do this the broader discussion although I'm trying to paint the broad pictures so you can get the crazy ass lessons as part of this.

嗯，所以我接下来会问两个问题，然后会打开讨论，因为我想确保每个人都有机会加入更广泛的讨论中。虽然我试图勾勒一个广阔的画面，让你能理解这其中的一些疯狂的教训。

What do you think what do you think is going to happen vis-a-vis the application of AI to like these very important systems like for example financial markets you know because the very natural thing would be is say well let's let's do a high frequency quant trading system on top of this and other kinds of things what what is it is it just kind of be a neutral arms race is it is it what how do you have what what you thought and like it's almost like the life 3.0 yeah amegas point of view yeah um.

你认为将AI应用于像金融市场等这些非常重要的系统会发生什么事情？因为很自然的想法是，我们可以在这个基础之上建立高频量化交易系统等等。你认为这只是一种中立的竞争吗？还是有其他的一些情况？你在思考这个问题时的看法好像和《生命3.0》的阿梅加角度类似。

I mean I think it is going to just see been everywhere my basic model of the next decade is that uh the cost of intelligence the marginal cost of intelligence and the marginal cost of energy are going to trend rapidly towards zero like surprisingly far and and those I think are two of the major inputs into the cost of everything else except the cost of things we want to be expensive the status goods whatever and and I think you have to assume that's going to touch almost everything um because these like seismic shifts that happen when like the whole cost structure of society change which happened many times before um like the temptation is always to underestimate those uh so I wouldn't like make a high confidence prediction about anything that doesn't change a lot or where it doesn't get to be applied um but one of the things that it's important is it's not like the thing trends either trends all the way to zero they just trend towards there and so it's like someone will still be willing to spend a huge amount of money on computing energy they will just get like unimaginable amount of intelligence energy they'll just get unimaginable amounts about that and so like who's going to do that and where's it going to get the weirdest not because the cost comes way down but the amount spend actually goes way up yes the intersection of the two curves yeah you know the thing got ten or a hundred times cheaper in the cost of energy you know a hundred million times cheaper in the cost of intelligence and I was still willing to spend a thousand times more into days dollars like what happens then yep.

我觉得未来十年，智能和能源的边际成本将迅速趋近于零，这是我的基本模型，这对于除了我们想要昂贵的奢侈品之外的所有其他成本都会产生影响。我认为几乎所有方面都会受到影响，因为社会成本结构的整体变化是这样的巨大，以至于它们的影响经常被低估。我不能对那些不经常变化或无法应用这种影响的事物做出高置信度的预测，但很重要的一点是，这些趋势并不是绝对趋势，而是会在接近零的极限处趋近于零。因此，某些人仍然会愿意在计算和能源上投入大量资金，他们将获得难以想象的智能和能源，那么谁会这样做，这种变革将会如何展开，这是最有趣的地方，因为人们花费的金额实际上会大幅上升。当两个曲线相交时，总体成本会降低十倍或一百倍，能源成本会降低一亿倍，智能成本会降低一百万倍，但今天人们仍然愿意花费相当数量的美元。那要是花费的金额增加，将会发生什么呢？

And then uh last of the buzzword bingo part of the the future questions metaverse an AI what do you what do you see coming in this you know I think there are like both independently cool things it's not like totally clear to me yeah other than like how AI will impact all computing yeah well obviously computing simulation environments agents possibly possibly entertainment certainly education right um you know like an AI tutor and so forth those those would be baseline but the question is is there anything that's occurred to you that's I I would bet that the metaverse turns out in the upside case then which I think has a reasonable chance to happen in the upside case the metaverse turns out to be more like something on the order of the iPhone like a new a new container for software and you know a new way a new computer interaction thing and AI turns out to be something on the order of like a legitimate technological revolution um and so I think it's more like how the metaverse is going to. fit into this like new world of AI than AI fit into the metaverse but low confidence TBD all right questions

然后，关于未来问题的元宇宙和人工智能的最后一部分噱头彩虹游戏，你认为会发生什么呢？我认为有一些很酷的事情，但除了人工智能对所有计算的影响之外，其他还不是很清楚。显然，计算机模拟环境、代理、娱乐，当然还有教育，比如人工智能辅导员等，都会成为基础，但问题是你是否想到了任何其他的东西。我打赌元宇宙在最好的情况下会变成另外一种iPhone，一种新的软件容器和计算机交互方式，而人工智能则可能成为一场真正的技术革命。因此，我认为更重要的是元宇宙如何适应这个全新的人工智能世界，而不是人工智能如何适应元宇宙，但我对答案不是很有信心，现在还需要待定。

hey there how do you see uh technologies uh foundational technologies like tpc3 affecting the pace of life science research specifically um you can group in medical research there and and sort of just quickening the iteration cycles and then what do you see as the rate limiter in life science research and sort of where we won't be able to get past because they're just like laws of nature yeah something like that um so I I think the current leo available models are kind of not good enough to have like made a big impact on the field at least that's what like most like life sciences researchers have told me they've all looked at it now I guess a little helpful in some cases um there's been some promising work in genomics but like stuff on a bench top hasn't really impacted it I think that's going to change and I think uh they this is one of these areas where there will be these like you know new hundred billion to trillion dollar companies started and those those areas are rare but like when you can really change the way that if you if you can really make like a you know future of pharma company that is just hundreds of times better than what's out there today that that's going to be really different um as you mentioned there still will be like the rate limit of like bio has to run its own thing and human trials take over long they take and that's so I think an interesting cut of this is like where can you avoid that like where are the the synthetic bio companies that I've seen that have been most interesting to the ones that find a way to like make the cycle time super fast um and that that benefits like an AI that's giving you a lot of good ideas but you've still got to test them which is where things are right now um I'm a huge believer first startups that like the thing you want is low costs and fast cycle times and if you have those you can then compete as a startup against the big incumbents uh and so like I wouldn't go pick like cardiac disease is my first thing to go after right now with like at this kind of new kind of company um but you know using bio to manufacture something that sounds great uh I think the other thing is the simulators are still so bad and if I were and if I were a biomeats AI startup I would certainly try to work on that somehow when you think the AI tech will help create itself it's almost like a self improvement will help make the simulator significantly better um people are working on that now uh I don't know quite how it's going but you know there's very smart people are very optimistic about that yep other questions and I can keep going on questions I just want to make a sure you guys had a chance of this oh here yes great uh Micah's coming awesome thank you

嘿，你如何看待像tpc3这样的基础技术对于生命科学研究的影响，特别是在医学研究中，加快了迭代循环速度。同时，你认为生命科学研究的限制因素在哪些方面，因为有些自然法则我们无法突破。我认为目前的可用模型不够好，至少大多数生命科学研究人员告诉我是这样的。虽然有些地方有所帮助，但在基因组学等方面的研究仍处于有待突破的状态。但我认为这种情况将会改变，这是一个会催生产生数千亿甚至万亿美元新公司的领域。如果能够真正改变这一领域，开创一家未来的创药公司，比现在的公司好上百倍，那将是非常不同的。但是，生物学肯定也有其运转的速度极限，人体试验也需要很长时间，这是我们无法突破的自然法则。目前有些被称为“合成生物技术”的公司有趣之处在于，他们寻找了一种快速的周期路径，这有助于那些想通过人工智能获得许多好想法，但仍需要测试它们的人。我非常看好创业公司，因为你需要低成本和快速迭代周期，有了这些，你就可以在创业公司和大公司之间竞争。因此，我不会选择立刻去研究心脏病，而是选择将生物制造与某些新型公司结合起来。此外，目前的生物模拟器还有很大的提升空间，如果我是一家拥有生物人工智能初创公司的创始人，我肯定会尝试解决这个问题。当你认为人工智能技术有助于自我提高时，它几乎会帮助改善模拟器的性能。人们正在研究相关技术，但我不太清楚进展如何。还有什么问题吗，如果有的话我可以继续谈下去，我只是想确定你们都有机会参与讨论。非常感谢。

focus on niche applications of these models, or will we see broader platforms that enable easier access to these models for a wider array of use cases?

我们会看到更多关注这些模型的细分应用，还是会出现更广泛的平台，以便更多用例更容易地使用这些模型？

I was curious what aspects of life do you think won't be changed by AI? Um, sort of all of the deep biological things like I think we will still really care about interaction with other people, we'll still have fun. And the reward systems of our brain are still going to work the same way. We're still going to have the same drives to create new things and compete for silly status and form families and whatever. So I think the stuff that people cared about 50,000 years ago is more likely to be the stuff that people care about 100 years from now than 100 years ago.

我很好奇你认为哪些方面的生活不会受到人工智能的改变？嗯，所有深层次的生物学因素，比如我认为我们仍然非常关心与他人的互动，我们仍然会玩乐。我们大脑的奖励系统仍然会以相同的方式工作，我们仍然会有创造新事物和追求低级趣味的竞争驱动力，还会成家立业等等。所以我认为人们50000年前关心的事情，比100年前更有可能是人们100年后所关心的事情。

As they amplify on that before we get to whatever the next question is, what do you think are the best utopian science fiction universes so far?

在我们进行下一个问题之前，他们再详细阐述一下，你认为到目前为止最好的乌托邦科幻世界是什么？

Good question. Star Trek is pretty good, honestly. Like, I do like all of the ones that are sort of like, we turn our focus to exploring and understanding the universe as much as we can. It's not a utopian one. Maybe.

好问题。星际迷航其实挺好的。就像我们把注意力转向探索和尽可能了解宇宙的那些剧集，我喜欢它们都很。它不是乌托邦。也许。

I think the last question is like an incredible short story. Yeah, that came up. Yeah, mine. Yep. I was expecting you to say, Ian Banks, on the culture. Those are great. I think science fiction is like, there's not like one sci-fi universe that I could point to and say, I think all of this is great. But like the optimistic corner of sci-fi, which is like a smallish corner, I'm excited about. Actually, I took a few days off to write a sci-fi story, and I had so much fun doing it, just about sort of like the optimistic case of AGI, that it made me want to go read a bunch more. So I'm looking for recommendations of more to read now. Like the sort of less known stuff, if you have anything. I will get to some great thumb recommendations.

我觉得最后一个问题就像一个难以置信的短篇小说。是的，我提出来的。没错。我本来以为你会说伊恩·班克斯的《文明文化》系列小说。那些也很棒。我觉得科幻小说就像……没法指出一个我认为全部都很棒的科幻世界。但像科幻小说中那个乐观的角落就很让我兴奋，虽然那只是一个小小的角落。事实上，我请了几天假写了一篇科幻小说，并感到非常愉快，它大致上讲述了人工智能的乐观案例，这让我想去读更多的科幻小说。所以，如果你有更多不为人知的好书推荐，我非常期待。我会找到更多好推荐。

So in a similar vein, one of my favorite sci-fi books is called Childhood's End by Arthur Clark, from like the 60s, I think. And I guess the one sentence summary is aliens come to the Earth, try to save us, and they just take our kids and leave everything else. So I think we're optimistic from that. But yes, there's ascension into the overmind is meant to be more utopian. But yes. OK.

我最喜欢的科幻书之一叫做《童年终结》，是阿瑟·克拉克在六十年代写的，我觉得。我猜它的简洁概括是外星人到地球来，试图拯救我们，然后他们只带走了我们的孩子，什么都没留下。所以我认为从那里我们是乐观的。但是，升华到超意识是更乌托邦的想法。好的。

You may not read it that way, but yes.

你可能不这样看，但是是的。

Well, also in our current universe, our current situation, a lot of people think about family building and fertility. And some of us have different people of different ways of approaching this. But from where you stand, what do you see as the most promising solutions? It might not be a technological solution, but I'm curious what you think other than everyone having 10 kids. How do we? Of everyone having 10 kids? Yeah. How do you populate? How do you see family building coexisting with AGI at high tech?

嗯，在我们当前的宇宙和现实情况下，很多人都在考虑建立家庭和生育的问题。而且我们每个人都对这个问题有不同的思考方式。但是就你的角度来看，你认为哪些方法最有前途呢？可能不一定是技术方案，但我很好奇你的看法，除了每个人都生十个孩子之外。我们该怎么办？每个人都生十个孩子？是的，你如何增加人口？你认为家庭建设和高科技中的人工智能怎么共存呢？

This is like a question that comes up at Open AI a lot. How do I think about how should one think about having kids? I think no consensus answer to this. There are people who say, yeah, I'm not. I thought I was going to have kids, and I'm not going to, because of AGI. Like there's just, for all the obvious reasons, and I think some less obvious ones, there's people who say, well, it's going to be the only thing for me to do in 15, 20 years. So of course, I'm going to have a big family. That's what I'm going to spend my time doing. I'll just raise great kids. And I think that's what will bring me to fulfillment. I think, as always, it is a personal decision.

这就像是 Open AI 经常会遇到的一个问题。我们该如何思考生孩子的问题呢？对此我认为没有共识性的答案。有些人说他们不打算生孩子了，理由是 AGI。这是非常明显的，也有一些不那么明显的理由。还有些人说，在未来 15 到 20 年里，生孩子可能是唯一的选择，所以他们会拥有一个大家庭，他们会花时间抚养孩子，并为此感到满足。我认为，这永远都是个人决定。

I get very depressed when people are like, I'm not having kids because of AGI. The EA community is like, I'm not doing that, because they're all going to die. They're kind of like techno-opimists are like, well, it's just like, I want to merge into the AGI and go off exploring the universe. And it's going to be so wonderful. And I just want total freedom. But I think all of those I find quite depressing. I think having a lot of kids is great. I want to do that now more than I did, even more than I did when I was younger. And I'm excited for it.

当有人说“我不想生孩子是因为人工智能”，我会非常沮丧。EA社群也不是那样，他们认为所有人都会死亡。科技乐观主义者则认为“我想要融入人工智能，并探索宇宙，那将是非常美好的事情。我想要完全的自由”。但是我觉得这些都很令人沮丧。我认为要生很多孩子是很好的事情。现在我比年轻时更想要生孩子，我对此感到兴奋。

What do you think will be the way that most users interact with foundation models in five years? Do you think there'll be a number of verticalized AIs start-ups that essentially focus on niche applications of these models, or will we see broader platforms that enable easier access to these models for a wider array of use cases? have adapted and fine-team models to an industry, or do you think prompt engineering will be something many organizations have as an in-house function?

你认为五年后大多数用户与基础模型互动的方式会是什么？你觉得会有一些垂直AI创业公司，专注于这些模型的细分应用，还是会看到更广泛的平台，使更广泛的用例更容易地访问这些模型？你认为企业是否会适应并精细地调整模型，还是快速工程将成为许多组织的内部功能？

I don't think we'll still be doing prompt engineering in five years. I think it'll just be like, it will be integrated everywhere. But you will just like, either with text or voice, depending on the context, you will just like interface in language and get the computer to do whatever you want. And that will apply to generate an image where maybe we still do a little bit of prompt engineering. But it's kind of just going to get it to go off and do this research for me and do this complicated thing. Or just be my therapist and help me figure out to make my life better or go use my computer for me and do this thing or any number of other things. But I think the fundamental interface will be natural language.

我不认为五年后我们还会像现在这样进行快速工程。我认为它会被整合到每个地方。但你只需要用文字或语音来接口，根据上下文的不同，你可以使用语言与计算机交互，让它完成你想要的任何事情。这将适用于生成图像，也许我们仍需要进行一些快速工程。但这将变得更加智能，可以帮助我进行研究或完成复杂的工作。或者只是成为我的治疗师，帮助我改善生活，或帮我使用电脑完成某些任务或其他无数的事情。但我认为，基本的接口将是自然语言。

Let me actually push on that a little bit before we give the next question, which is, I mean, to some degree, just like we have a wide range of human talents right now, and taking, for example, a dolly, when you have a great visual thinker, they can get a lot more out of dolly because they know how to think more, they know how to iterate the loop through the test. Don't you think that will be a general truth about most of these things? So it isn't that, while it will be natural languages the way you're doing it, it will be, there will be almost an evolving set of human talents about going that extra mile.

让我在我们开始下一个问题之前再深入探讨一下。这就像我们现在拥有广泛的人类才能范围一样。例如，当你有一个很棒的视觉思考者和一个手推车（dolly）时，他们能够得到更多，因为他们知道如何更好地思考，他们知道如何通过测试迭代循环。你不认为这将是大多数事情的一般真理吗？因此，这不是因为你正在进行的是自然语言，而是在那个额外的里程碑上有一个几乎是进化的人类才能集。

100%. I just hope it's not figuring out how to hack the prompt by adding one magic word to the end that changes everything else. What will matter is the quality of ideas and the understanding of what you want. So the artist will still do the best with image generation, but not because they figured out to add this one magic word at the end of it, because they were just able to articulate it with a creative eye that I don't have certain. They have as a vision and how their visual thinking and iterating through it. Obviously, it'll be that word or prompt now, but it'll iterate to better.

我希望这不是一种通过在最后添加一个神奇的单词来改变其他所有内容的方式来破解提示。重要的是想法的质量和您的理解。因此，艺术家仍将尽最大努力生成图像，但不是因为他们想到在结尾添加一个这样的神奇词语，而是因为他们能够用一种我无法把握的创造性眼光阐述它，并具有视觉思维和通过迭代实现它所需的能力。显然，现在这个单词或提示是关键，但它将不断得到改善。

All right, at least we have a question here. Hey, thanks so much. I think the term AGI is used thrown around a lot. And sometimes I've noticed in my own discussions the sources of confusion has just come from people having different definitions of AGI, and so it can kind of be the magic box where everyone just kind of projects with their ideas onto it. And I just want to get a sense for me. Like, how would you define AGI and how do you think you'll know what would be when that early? That is.

好的，至少我们有一个问题在这里。嘿，非常感谢你。我认为AGI这个术语经常被提及。有时我注意到在我的讨论中，混淆的来源就是因为人们对AGI有不同的定义，所以它可以成为一个魔盒，每个人都可以将自己的想法投射到它上面。我只是想了解一下。比如，你怎么定义AGI，你认为什么时候会知道它早期的状态是什么。

It's a great point. I think there's a lot of valid definitions to this. But for me, AGI is basically the equivalent of a median human that you could hire as a coworker. So they could say, do anything that you'd be happy with a remote coworker doing, just behind a computer. Which includes learning how to go be a doctor, learning how to go be a very competent coder. There's a lot of stuff that a median human is capable of getting good at. And I think one of the skills of an AGI is not any particular milestone, but the meta skill of learning to figure things out and that it can go decide to get good at whatever you need. So for me, that's kind of like AGI. And then super intelligence is when it's like smarter than all of humanity put together.

这是一个非常有价值的观点。我认为这个定义有很多可取之处。但对我来说，AGI基本上等价于一个中等人类，你可以雇用他作为合作伙伴。所以他们可以做任何你会对远程合作伙伴做的事情，只是在电脑后面完成。这包括学习如何成为医生，学习如何成为一个非常有能力的编码人员。一个中等人类能够擅长很多事情。我认为AGI的一项技能不是任何特定的里程碑，而是学习解决问题的元技能，并且它可以决定变得擅长任何你需要的事情。所以对我来说，这就像AGI。然后，超级智能是指比整个人类集合还聪明的智能。

So we have, do you have a question? Yep. Great. Thanks. Just what would you say are in the next 20, 30 years are some of the main societal issues that will arise as AGI continues to grow? And what can we do today to mitigate those issues?

那么，我们有问题吗？有。好的，谢谢。在未来的20到30年中，随着人工智能的持续发展，您认为将出现哪些主要的社会问题？我们今天可以做些什么来缓解这些问题呢？

Obviously, the economic impacts are huge. And I think if it is as divergent as I think it could be for some people doing incredibly well in others, not, I think society just won't tolerate it this time. And so figuring out when we're going to disrupt so much of economic activity. And even if it's not all disrupted by 20 or 30 years from now, I think it'll be clear that it's all going to be.

显然，经济影响是巨大的。我认为，如果它像我认为的那样对一些人表现得非常好，对另一些人则不好，那么这次社会就不会容忍它。因此，我们需要弄清楚何时会有如此多的经济活动被打破。即使它不会在20或30年内全部破坏，我认为很明显，它最终都将被破坏。

And what is the new social contract? My guess is that the things that we'll have to figure out are how we think about fairly distributing wealth, access to AGI systems, which will be the commodity of the realm, and governance, how we collectively decide what they can do, what they don't do, things like that. And I think figuring out the answer to those questions is going to just be huge.

那么新社会契约是什么？我猜我们必须考虑公平分配财富、获取AGI系统（将成为主要商品）以及治理方式，即我们如何共同决定其行为方式。我认为找到这些问题的答案将是极其重要的。

I'm optimistic that people will figure out how to spend their time and be very fulfilled. I think people worry about that in a little bit of a silly way. I'm sure what people do will be very different, but we always solve this problem. But I do think like the concept of wealth and access and governance, those are all going to be huge. to change. And how we address those will be huge.

我很乐观地认为人们会想出如何度过他们的时间并且很充实。我觉得人们有点傻傻地担心这个问题。我确信人们做的事情会非常不同，但我们总是能解决这个问题。但我认为像财富、接触和治理的概念，这些都将是巨大的改变。我们如何处理这些问题将会是巨大的挑战。

Actually, one thing I don't know would love love. Devs, you can share that. But one of the things I love about what OpenAI and you guys are doing is when you think about these questions a lot themselves, and they initiate some research. So you've initiated some research on this stuff. Yeah, so we run the largest UBI experiment in the world. I don't think that is we have a year and a quarter left in a five-year project. I don't think that's the only solution, but I think it's a great thing to be doing.

实际上，有件事我不知道，但很想知道。开发人员，你们可以分享一下。但我喜欢OpenAI和你们所做的事情之一就是，当你们自己思考这些问题，还会启动一些研究。所以你们已经启动了一些研究。是的，我们正在进行全球最大规模的UBI实验。我不认为这是唯一的解决办法，但我认为这是一件伟大的事情。我们还剩下一年零四分之一的时间在这个为期五年的项目中。

And I think we should have 10 more things like that that we try. We also try different ways to get input from a lot of the groups that we think will be most affected. And see how we can do that early in the cycle. We've explored more recently how this technology can be used for rescilling people that are going to be impacted early. We'll try to do a lot more stuff like that, too. So the organization is actually, in fact, these are great questions addressing them and actually doing a bunch of interesting research on it.

我认为我们应该有像这样的10个东西来尝试。我们也会尝试不同的方法，以获取很多我们认为会受到最大影响的群体的意见。并且尽早在这个周期内看看我们该如何做到这一点。我们最近探索了这种技术如何用于培训那些将受到早期影响的人。我们也会尝试做更多类似的事情。因此，组织实际上正在着手解决这些重要问题，并进行了大量有趣的研究。

So next question. Hi, yes. So creativity came up today in several of the panels. And it seems to me that the way it's being used, you have tools for human creators and go and expand human creativity. So where do you think the line is between these tools to allow a creator to be more productive and artificial creativity, the sequence of creativity itself?

下一个问题。嗨，是的。在几个小组讨论中，创意今天成为了一个热门话题。看起来，这些工具是为人类创作者设计的，可以扩展人类的创造力。那么，您认为这些工具能让创作者更高效地工作，还是会产生人工的创造力，即创造力本身的过程？

So I think, and I think we're seeing this now that tools for creatives, that is going to be the great application of AI in the short term. People love it. It's really helpful. And I think it is, at least in what we're seeing so far, not replacing it is mostly enhancing. It's replacing, in some cases, but for the majority of the kind of work that people in these fields want to be doing, it's enhancing. And I think we'll see that trend continue for a long time.

我觉得，我也认为我们现在看到的是，对于创意人员的工具，这将是人工智能在短期内的伟大应用。人们喜欢它，这真的很有帮助。我认为，至少在我们目前看到的情况下，它并没有取代，而是大多数情况下是增强了人的工作。虽然有一些情况下是取代了，但对于这些领域中的人想要做的大部分工作来说，它都是增强了人的工作。我认为这种趋势将会持续很长一段时间。

Eventually, yeah, it probably is just like, we look at 100 years. OK, it can do the whole creative job. I think it's interesting that if you asked people 10 years ago about how AI was going to have an impact with a lot of confidence from almost most people, you would have heard, first it's going to come for the blue-collar jobs, working the factories, truck drivers, whatever. Then it will come for the kind of like the low-skill white-collar jobs, then the very high-skill, like really high IQ, white-collar jobs, like a programmer, whatever. And then very last of all, and maybe never, it's going to take the creative jobs.

最终，是的，也许就像我们看待一百年一样。好的，它可以完成整个创造性工作。我认为有趣的是，如果你10年前问人们AI将如何影响的话，几乎大多数人都有信心，你会听到，首先它会取代蓝领工作，像在工厂、卡车司机等岗位。然后，它会取代那些低技能的白领工作，然后是非常高技能、需要非常高智商的白领工作，比如程序员等。而且最后，甚至永远也不可能，它将取代创意工作。

And it's really gone exactly, and it's going exactly the other direction. And I think this, there's an interesting reminder in here, generally, about how hard predictions are.

它已经完全消失了，它正在完全朝另一个方向发展。我认为这里有一个有趣的提示，通常是关于预测的难度。

But more specifically, more not always very aware, maybe, even ourselves of what skills are hard and easy, what uses most of our brain and what doesn't, or how difficult bodies are to control or make, or whatever.

但更具体地说，我们中有些人可能并不总是很清楚哪些技能很难，哪些技能很容易，哪些技能需要大量的脑力投入，哪些技能不需要，或者我们在控制和制造身体时遇到了多么困难的挑战，等等。

We have one more question over here. Hey, thanks for being here. So you mentioned that you would be skeptical of any startup trying to train the old language model, and it would look to understand more.

我们这边还有一个问题。嘿，感谢您在这里参与。所以您提到您会对任何尝试训练旧语言模型的初创公司持怀疑态度，而会寻求更多了解。

So what I have heard, and which might be wrong, is that large language models depend on data and compute. And any startup can access to the same amount of data, because it's just like internet data.

我听说大型语言模型依赖于数据和计算机，但我可能错了。任何初创公司都可以访问到同样数量的数据，因为这就像互联网数据一样。

And compute, like, different companies might have different amount of compute, but I guess they're big players because they're same amount of compute. So how good a large language model startup differentiate from another? How would the startup differentiate from another? How good one large language model startup differentiate from another?

就像不同的公司有不同数量的计算机一样，它们可能是大型参与者，因为它们有相同数量的计算机。因此，一个大型语言模型创业公司如何区别于另一个？这个创业公司要如何区别于另一个？一个大型语言模型创业公司如何区别于另一个？

I think it'll be this middle layer. I think in some sense, the startups will train their own models, just not from the beginning. They will take base models that are hugely trained with a gigantic amount of compute and data, and then they will train on top of those to create the model for each vertical.

我觉得这个中间层将是这样的。我认为在某种意义上，初创公司将训练他们自己的模型，只是不是从头开始。他们将采用基础模型，并通过大量计算和数据进行深度训练，然后在此基础上进行训练，以创建每个领域的模型。

And so in some sense, they are training their own models, just not from scratch, but they're doing the 1% of training that really matters for whatever this use case is going to be. Those startups, I think, there will be hugely successful and very differentiated startups there.

所以在某种程度上，他们正在训练自己的模型，只不过不是从头开始，而是他们正在做这个使用案例至关重要的1%的训练。我认为，这些创业公司将会非常成功，并且将有很大的差异化。

But that'll be about the data flywheel that the startup is able to do, the all of the pieces on top and below. Like this could include prompt engineering for a while at whatever, the core base model. I think that's just going to get to complex and to expensive, and the world also just doesn't make enough chips.

但这将是关于初创公司能够做到的数据飞轮，所有上下的部件。例如，这可能包括在核心基础模型的任何地方进行实时工程。我认为这将变得过于复杂和昂贵，而且世界上也没有足够的芯片。

So Sam has a work thing he needs to get to. And as you probably can tell with a very far ranging thing, Sam always expands my batteries. And a little bit unlikely that when you're feeling depressed, whether it's kids and unhills, you're the person I was turned to for a while. I appreciate that. Yes. So anyway.

所以，山姆有个工作要去处理。你可能已经发现，随着很多事情的不断发展，山姆总是让我疲惫不堪。而且有点不太可能的是，当你感到沮丧时，无论是因为孩子还是因为不顺心，你是我会寻求帮助的人。我很感激。是的，总之。

I think no one knows. We're sitting on this precipice of AI and like people like it's either going to be really great or really terrible. You may as well like you've got to plan for the worst.

我觉得没有人知道。我们坐在AI的悬崖上，就像人们一样，这可能会非常好，也可能会非常可怕。你最好计划最坏的情况。

You certainly like it's not a strategy to say it's all going to be OK. But you may as well like emotionally feel like we're going to get to the great future. And we're playing part as you can to get there and play for it, rather than like act from this place of like fear and despair all the time.

你肯定喜欢这种说法，即“一切都会好起来”不是一种策略。但你也可能会像情感上感觉我们会走向美好未来一样喜欢这种说法。我们正在尽自己的一份力，去实现这一目标并为它而奋斗，而不是总是从害怕和绝望的角度出发。如果必要的话，可适当改写。

Because if we acted from a place of fear and paranoia, we would not be where we are today. So let's thank Sam for spending dinner with us. Thank you.

因为如果我们从恐惧和偏执的角度出发，我们今天就不会处于这样的位置。所以让我们感谢Sam与我们共进晚餐。谢谢。

That concludes this episode of Grey Matter. If you're interested in all things AI, check out the rest of our Intelligent Future content devoted to the topic.

这就结束了本集的“灰质”节目。如果您对人工智能相关的事情感兴趣，请查看我们其他的“智能未来”内容，专门讨论这个话题。

You can even hear the technology in action with Hoffman's Fireside Chatbot series, where he talks about AI with ChatGPT. And if you aren't already a subscriber to Grey Matter, please sign up wherever you get your podcasts. I'm Heather Mack. Thanks for listening.

你甚至可以听到霍夫曼的“炉边聊天机器人”系列中的技术，在那里他与 ChatGPT 谈论人工智能。如果你还没有订阅 Grey Matter，请在任何获取播客的地方进行订阅。我是 Heather Mack。感谢您的收听。