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Prompt and Process with Ethan Mollick [AI miniseries]

发布时间 2023-07-05 08:30:00    来源

摘要

What if you could tweak the questions you ask ChatGPT and make you ten times more effective or productive? Ethan Mollick is an Associate Professor at the Wharton School of the University of Pennsylvania. He studies and teaches innovation and entrepreneurship, and also examines the effects of artificial intelligence on work and education. He’s also an insightful and leading voice in the AI space, especially on Twitter. In the final episode of our AI miniseries, Reid, Aria, and Ethan discuss how society can prepare for a future with AI and how AI enhances learning in—and beyond—the classroom. Ethan shares his specific advice for people of varying experience with AI who are looking to get more out of their interactions with ChatGPT. Read the transcript of this episode here. For more info on the podcast and transcripts of all of the episodes, visit www.possible.fm/podcast.   Topics: Hellos and intros - 3:59 Changes that can make AI more exciting—and functional - 5:36  Considering more futures involving AI - 8:00 Three tiers of prompting advice - 11:42 Novel applications of AI - 15:34 What education is needed to seize AI’s potential? - 19:16  AI as a tool for reflection - 25:10 When AI falls into scripts - 26:35 AI in the classroom - 28:24 AI for lifelong learners - 38:19 The humanization of AI - 41:11  How we elevate humanity using AI - 43:39 Rapid fire questions - 47:41 Debrief with Reid and Aria - 53:47 Possible is a podcast that sketches out the brightest version of the future—and what it will take to get there. Most of all, it asks: what if, in the future, everything breaks humanity's way? In its first season, hosts Reid Hoffman and Aria Finger spoke with visionaries across many fields, from climate science to criminal justice, and from entertainment to education. For this special miniseries, they’re speaking with expert builders and skilled users of artificial intelligence. These conversations also feature another kind of guest, AI. Whether it’s Inflection’s Pi or OpenAI’s GPT-4, each episode will include an AI-generated element to spark discussion.  Possible is produced by Wonder Media Network and hosted by Reid Hoffman and Aria Finger. Our showrunner is Shaun Young. Possible is produced by Edie Allard and Sara Schleede. Jenny Kaplan is our Executive Producer and Editor. Special thanks to Surya Yalamanchili, Ian Alas, Greg Beato and Ben Relles.

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中英文字稿  

We need to consider more futures that we're considering right now. I think everybody's mental model is either AI never improves past today because we're not good at exponential change and we're not good at seeing that. But I also think a lot of people are also worried about the other case which is like machine God takes over the world which we obviously should worry about. But there's a lot of things in between those two worlds that are profoundly changing what we do. It's two times better, what if it's ten times better?
我们需要考虑更多我们现在正在考虑的未来。我认为每个人都有两种心理模式，一种是认为人工智能永远不会超越今天，因为我们不擅长指数级的变化，也不善于看到这一点。但我也认为很多人也担心另一种情况，即机器之神掌控世界，这是我们显然应该担心的。但在这两种世界之间，有很多令我们所做的事情发生深刻改变的事情。如果它是两倍好，那它如果是十倍好呢？

Hi, I'm Reed Hoffman. And I'm Aria Fingar. We want to know what happens if, in the future, everything breaks humanity's way. In our first season, we spoke with visionaries across many fields, from climate science to criminal justice and from entertainment to education. For this special mini-series, we're speaking with expert builders and skilled users of artificial intelligence. They use hardware, software, and their own creativity to help individuals use AI to better their personal everyday lives. These conversations also feature another kind of guest, AI. Whether it's inflections pie or open AI's GBD4, each episode will include an AI-generated element to spark discussion. You can find these additions down on the show notes.
大家好，我是里德·霍夫曼，我是阿里亚·芬格。我们想知道，如果未来一切都对人类有利，会发生什么。在我们的第一季中，我们与各个领域的先知进行了对话，从气候科学到刑事司法，从娱乐到教育。对于这个特别的迷你系列，我们正在与专业建筑师和熟练的人工智能用户对话。他们使用硬件、软件和自己的创造力，帮助个人运用人工智能改善日常生活。这些对话还包括另一种特殊的嘉宾，人工智能。不管是Inflections Pie还是Open AI的GBD4，每一集都将包含一个由人工智能生成的元素来引发讨论。您可以在节目说明中找到这些附加内容。

In each episode, we seek out the brightest version of the future and learn what it takes to get there. This is possible. As everyone knows, this summer we are doing our mini-arc on AI and the first episode of the summer series was about personal AI and software. The second we got to talk about personal AI and hardware, and this last episode is about personal AI and the individual. It is the most tactical yet.
在每一集中，我们追求未来最光明的版本，并学习到达那里所需付出的努力。这是可能的。正如大家所知，今年夏天我们在AI上进行了一系列的迷你剧。夏季系列的第一集是关于个人 AI 和软件的。第二集我们开始谈论个人 AI 和硬件，而最后一集则是关于个人 AI 和个体的。这是迄今为止最具实践性的一集。

I am so excited about our guest because our relationship with Ethan Mollick started with a cold email. He had been tweeting and talking about AI and everyone on our team had said, oh my gosh, you got to follow this guy on Twitter. I just sent him a cold email and said, hey, would you chat with me? He was so kind and we got on a call and his energy and excitement for AI just jumped through the computer screen. Just so delighted to have him on the pod so that everyone can hear his excitement and enthusiasm for this topic. I think more or less I get set more tweets by him than by anybody else because he's like, oh, you should check this out. Oh, this is really important. Oh, and it was like, actually you get to know him and you go, wow, you can't possibly be that good. It's so amazing that when I get to talking with him, it can't be that good. I'm really looking forward to this because I know you through your tweets. Now let me talk to you. This will be a very interesting experiment, almost like GPT, like putting it in a prompt and seeing what comes out. If people are listening and thinking, yeah, this is all great, but what does AI mean for me? How can I improve? How can I get better? What can I do? Ethan Moloch is the person to talk about it, so thrilled that he's going to be doing that.
我对我们的嘉宾感到非常兴奋，因为我们与伊森·莫利克的关系始于一封冷冻邮件。他一直在推特上发推和谈论人工智能，我们团队的每个人都说，哇塞，你必须关注这个人的Twitter。我给他发送了一封冷冻邮件，说，嘿，能和我聊聊吗？他非常友善，我们通了电话，他对人工智能的热情和兴奋直接传递到了电脑屏幕上。非常高兴能够让他参加这次节目，这样每个人都可以听到他对这个话题的兴奋和热情。我觉得我从他那里收到的推特比任何其他人都多，因为他就像说，哦，你应该看看这个。哦，这个非常重要。哦，而实际上，你了解他之后会惊叹不已，你不可能那么好。当我和他聊天时，我真的很期待，它不可能那么好。我真的很期待这次对话，因为我通过你的推特了解你。现在让我和你谈谈。这将是一个非常有趣的实验，就像GPT一样，将其放入提示中看看会出现什么。如果听众们在思考，是的，这一切都很棒，但人工智能对我意味着什么？我如何提高？我该怎么办？伊森·莫利克就是一个可以谈论这些问题的人，非常激动他将会那样做。

So this is the last and final episode of our series on AI and the person also. Anyone listening, please do subscribe because then you will be the first to hear about our new fall season. Ethan Moloch is an associate professor at the Wharton School of the University of Pennsylvania where he studies and teaches innovation and entrepreneurship and also examines the effects of artificial intelligence on work and education. He also leads Wharton Interactive, an effort to democratize education using games, simulations, and AI. Here's our conversation with Ethan Moloch.
这是我们关于人工智能和个体的系列节目的最后一集。听众们，请务必订阅，因为这样您将会第一个收到我们新的秋季系列的消息。伊桑·摩洛克是宾夕法尼亚大学沃顿商学院的副教授，他研究和教授创新和创业，并研究人工智能对工作和教育的影响。他还领导着沃顿互动，致力于通过游戏、模拟和人工智能来推动教育的民主化。以下是我们与伊桑·摩洛克的对话。

Ethan, thank you so much for being here today. It's so lovely to see you again. And we have a Slack channel at work that is all about everything AI. So every day there's 10, 20, 30, what are the latest new AI things of the day? And your Twitter is basically every other post. So my question for you is how did you get here? How did you become the guy who is at the center of AI experimenting and playing with it? We'd love to hear that story.
伊桑，非常感谢你今天能来这里。再次见到你真的很高兴。在我们的工作中，有一个Slack频道专门讨论人工智能的一切。所以每天都有10、20、30个最新的人工智能事物。而你的Twitter账号上基本上每隔一篇就有相关帖子。所以我的问题是，你是如何走到这一步的？你是如何成为在人工智能领域进行实验和探索的中心人物的？我们很想听听你的故事。

So it's actually a weird story. AI adjacent, but not really an AI person. So I back in grad school, I did a lot of work at the Media Lab with the AI group at that point, which is like Marvin Minsky, who pushed a bunch of people like that, where I wasn't the technical person. I was sort of like the business school representative there, trying to communicate AI to other people. And sort of been around that AI community for a long time. My real passion has been how do we increase people's ability to learn how to increase education through interactive tools. So I've been doing that for a very long time and playing with AI on the side because it's always been promising, but not quite there.
所以其实这是一个奇怪的故事。虽然与人工智能相关，但并不是一个真正的人工智能专家。所以在研究生阶段，我在媒体实验室和当时的人工智能团队一起做了很多工作，比如马文·明斯基等人推动了一批像他这样的人，我并不是技术人员，而是商学院的代表，试图向其他人传达人工智能的概念。我在人工智能社区里一直存在，并且一直以来，我的真正热情一直是如何通过互动工具提高人们的学习能力，促进教育的发展。所以我已经做了很长一段时间，并且一直在与人工智能打交道，因为它总是有着巨大的潜力，但还没有达到那个阶段。

So I was already assigned to my students assignments like cheat with AI from the more primitive GPT-3. And we were kind of in the middle of that cheat with AI assignment when chat came out. And I was like, oh, this is interesting. And then over the course of the day, I have a whole series of tweets on it where I'm like, oh my god, this is really interesting. Wait, this is insanely interesting. And then by the next sort of Tuesday, I was teaching my class and I sort of introduced it to class by the end of my first entrepreneurship class I was teaching. I had students who were already coding with it. And you know, usually I was like, okay, we've hit a big deal here. I sort of descended into its sideways from an education and interactivity viewpoint.
所以，我已经被分配给我的学生的作业，如与更原始的GPT-3进行作弊。当Chat出现时，我们正处于与AI作弊作业的中间阶段。我当时觉得这很有趣。然后，在接下来的一天里，我发了一系列的推文，我觉得这非常有趣。等等，这太有趣了。到了下一个周二，我在教我的课时，我介绍给了学生。而且，你知道的，通常我会说，我们在这里取得了一项重大进展。从教育和互动的角度来看，我对它的观点有所偏差。

You know, one of the things that I appreciate about you being a power user, chat GPT-4, being barred, maybe pie even, I don't know, I'd be very curious to get your feedback on pie. Product request, hopes, designs, what do you think of the current state of the art and what would move you from 11 out of 10 excited to 20 out of 10 excited? This is a universal tool that's available to everybody and there's so much debate over what happens next and how much smarter will this get. Well we've already completely disrupted work and education, but the tools aren't really supporting work and education use. You kind of have to work around it. You kind of have to hack a chatbot to produce an essay for you or do good work for you. And I think that some of this is really about that learning interface. Like it's a pretty hostile system. If you don't know it to start using it, people bounce off of AI very, very quickly for a wide variety of reasons or get down rabbit holes. And I think to me a lot of this is really about how do we build the education to this? How do you get AI to help people use AI better rather than necessarily even making the tools more advanced for all the good and bad that will do? I mean I think that's such a good point.
你知道的，作为一个强大的用户，聊天机器人GPT-4，可能还有派，我不确定，我很想知道你对派的看法。产品需求、希望、设计，你觉得现在的技术水平如何，有什么能让你从11分的兴奋提升到20分的兴奋呢？这是一个普遍适用于所有人的工具，关于接下来会发生什么以及这个工具会变得多么智能，有着很多争议。我们已经完全改变了工作和教育，但工具并没有真正支持工作和教育的使用。你需要绕过它，你需要“黑掉”一个聊天机器人为你写作文或做好的工作。我认为其中的一些问题实际上与学习接口有关。如果你不了解它，这是一个相当敌对的系统，人们很快就会因为各种原因而被AI所排斥，或者陷入困境。对我来说，这实际上与我们如何建立教育机制有关。如何让人工智能帮助人们更好地使用人工智能，而不仅仅是为了所有这些好坏而使工具更先进？我认为这是一个非常好的观点。

Like the chat interface was such an on-ramp to people using it. Like that form was great. But to your point, the fact that you have to create all of these, here's how to hack the system, here's this special prob. Like that's a problem for getting someone who's new to the system, especially because so in this summer arc for possible, we're talking about not necessarily sort of the sweeping societal changes, but how will AI impact your daily life? Like what are you most excited about to see AI transform our daily personal lives? I mean there's so much there, right? Like this is where the Nexus is like both exciting and kind of terrifying, right? Like I tend to, you know, there's a lot of jobs that are really high quality jobs that are, you know, not so much jobs but bundles of tasks that are under threat. And there's a lot of stuff that, you know, looks really good.
就像聊天界面是人们使用它的入口一样。就像那个表格很棒。但是按照你的观点，你必须创建所有这些，这是如何入侵系统的，这是这个特殊问题。这对于刚接触这个系统的人来说是个问题，尤其是因为在这个可能性的夏季弧中，我们谈论的不一定是全面的社会变革，而是人工智能如何影响你的日常生活？你对看到人工智能如何转变我们的日常生活最感兴奋的是什么？我是说那里面有很多东西，对吧？这就是 Nexus 既令人兴奋又令人害怕的地方，对吧？我倾向于认为，有很多高质量的工作，不仅仅是工作，而是一系列的任务都面临威胁。还有很多看起来很不错的东西。

From my perspective, entrepreneurship professor, right, this is like the absolute sweet spot because a third of Americans have an idea for a startup and they don't launch them. And they don't even do any research. So the idea of having a tutor or somebody push you along, a co-founder of sorts, hugely helpful, right? And then on the other side is an educator. I mean, you know, suddenly we have a tool available at 169 countries that is like the best, you know, education tool we ever released. And you know, we have to figure out how to unlock it. So I mean, I think for a potential democratizing opportunity, it's profoundly exciting in that sense.
从我的角度来看，作为一位创业教授，这个机会绝对是绝佳的。因为美国有三分之一的人有开办创业公司的想法，但他们没有实践，并且他们甚至没有进行任何研究。所以有一个导师或者某种方式的共同创始人来推动你，对他们来说是非常有帮助的，对吧？另一方面，对于教育者来说，我是指一个全球共有169个国家都能使用的教育工具，这是我们发布过的最好的教育工具。我们需要找到方法来解锁它。所以，我认为从民主化的机会来看，这是非常令人激动的。

So if you could wave a wand and kind of reorient the general public discourse on AI, what direction would you wave the wand in? What would you try to say like more of this, less of this? So I think that it's hard to say we shouldn't be worried about negative effects because we should. I think first of all, we need to consider more futures that we're considering right now. I think everybody's mental model is either AI never improves past today because we're not good at exponential change and we're not good at seeing that.
所以，如果你能够挥动魔杖，重新定位人们对人工智能的公众讨论，你会选择朝着什么方向挥动魔杖？你会尝试说更多什么，减少什么？所以我认为，我们很难说我们不应该担心负面影响，因为我们确实应该担心。首先，我认为我们需要考虑更多我们现在没有考虑到的未来。我觉得每个人都对人工智能的心理模型要么是人工智能永远不会超越今天的水平，因为我们不擅长指数级变化，也不擅长预测。

But I also think a lot of people are also worried about the other case, which is like machine God takes over the world, which we obviously should worry about, right? But there's a lot of things in between those two worlds that are profoundly changing what we do, right? What if it's two times better? What if it's 10 times better? Right now, if you're in the top 10% of whatever field or set you're in, you're definitely beating AI and AI can help you, but it's not going to outperform you, right? And everybody's got something they're really good at. AI is good at it. It's good at it. It's good is what you're really good at. That could change pretty quickly with a two to 10 times performance. And I think we have to consider that and worry about that piece.
但我也认为很多人同时也担心另一种情况，就是机器之神接管世界，这显然是我们应该担心的，对吧？但在这两种极端之间，有很多事情正在深刻地改变我们的生活，对吧？如果机器的表现是现在的两倍甚至十倍，那又会怎样呢？目前来看，如果你在任何领域或集合中处于前10%，你肯定能击败人工智能，并且人工智能可以帮助你，但它不会超越你，对吧？每个人都有自己擅长的东西。人工智能擅长的就是你擅长的东西。但是，如果其性能提高两倍到十倍的话，这种情况就可能迅速改变。我认为我们必须考虑并担心这一点。

And then the other part of the narrative I would change would also be thinking about, you know, the positive cases without being Pollyanna-ish about it or influencer about it. And people have to think about how does this make their lives better while still worrying about the ways it may make our lives worse. And I think trying to balance those two isn't happening very successfully in the world. The thing I would add to what you're saying is one part of the thesis that a lot of the worries and the critics have is they kind of say, well, the machines will eventually completely outstrip people and people won't even be able to be in combination. And they kind of use like the chest results as an example of that, which is there was a lacuna period where chess, a machine plus person was better and now machine is just better. I'm not sure if actually in fact that the person plus machine isn't a very long period in Indeed and maybe, you know, long from the viewpoint of by the time that that changes, the world's so different in so many different cases. We don't really know what it looks like.
然后，另一部分我会改变叙述的观点也会考虑到积极的情况，而不是过于乐观或受到影响者干预。人们必须思考这如何让他们的生活变得更好，同时仍然担心它可能使我们的生活变得更糟。而且，我认为在世界上，很难成功地平衡这两者。在你所说的观点上，我要补充的是很多担忧和批评的论点是，他们有点说，机器最终会完全超越人类，人类甚至无法与之结合。他们以象棋的结果作为例子，有一个间隔期，机器加人类的组合更好，现在机器要好得多。我不确定在事实上，机器与人类的组合不是一个很长的时期，也许从那个时候来看，世界在很多不同的情况下都发生了很大变化。我们实际上不知道它会是什么样子。

We can't fully imagine it. It's not like today plus, you know, God like machines. Even if you say, well, hey, it starts getting a lot better at writing investment memos than I am. And like you just said, you know, starting gun, you're going to write an investment memo in an hour. And I'm like, it goes, okay, it's better than you. But it's still like when you put us together, it's still better. Right. And that's the thing that I think that is the future that, you know, like I was doing with impromptu and you're doing with, you know, all of your various work, you know, including tweets and podcasts and writing and everything else is part of that reorientation of the future that I think is so important in the public discourse.
我们无法完全想象它。它并不像是今天再加上，你知道的那种神一样的机器。即使你说，嘿，它在写投资备忘录方面比我做得好多了。就像你刚才说的，你开枪起跑，你要在一个小时内写完一份投资备忘录，它可以做到，它比你好。但当我们把我们两个组合在一起时，仍然更好。对吧。这就是我认为是未来的东西，你知道，就像我在即兴创作中所做的，你在各种各样的工作中所做的，包括推文、播客、写作和其他所有事情，它们都是公众讨论中如此重要的未来重新定位的一部分。

I couldn't agree more. And I also think people underestimate social systems take a long time to change. Even if the system is infinitely better, there's still lots of human world pieces that are it will not be good at. I think people try and draw arbitrary bright lines like it's not going to be good at empathy. It's good at empathy. It's not good at innovation. It's good at innovation. Right. That's not really the way to view this. But there are, you know, there are perspectives and differences.
我非常赞同。而且我还认为人们低估了社会系统需要很长时间来改变的情况。即使系统变得无限好，仍然有很多人类世界的方面它无法擅长。我认为人们试图划定武断的界线，比如说它对共情不擅长，它对创新不擅长。但实际上，这不是正确的观点。当然，有不同的视角和差异存在。

And I think you're right. One of the things to realize is other things will have to change before a better AI is enough to change the entire world. Right. And you can see it just not that people are adopting people are bouncing off. This system, there is this idea that we're kind of rushing ahead. And again, that's where I think the emphasizing on the apocalyptic either saves us or kills us scenario is undermining how actual technical change works, which is, you know, this is a really fast change. Fast changes are still much slower than technologists think they are. And I agree with you. I think we have to, we have to be ready for a world where this changes gradual, embracing it matters, embracing your own tools matters. And I think that's a pretty profound point.
我认为你是对的。其中一件事情需要意识到的是，在更好的人工智能足够改变整个世界之前，其他事情必须发生改变。没错。你可以看到，不只是人们在接受，也有人在反对这个系统。有一种观点认为我们有点匆忙。而我认为，强调灾难性的情景，无论是拯救我们还是毁灭我们，都会削弱实际技术变革的工作方式。实际上，这是一个非常快速的变化。而快速的变化仍然比技术人员认为的要慢得多。我同意你的说法。我认为我们必须准备好迎接这个变化逐渐发生的世界，重视它是很重要的，重视你自己的工具也很重要。我认为这是一个非常深刻的观点。

So let's take that from the very high level to the very specific, what kind of prompts or sequence of prompts would you suggest for? And I'm going to give all three, but like, let's let's answer each three separately, a completely new user of chat GBT or, you know, pick your favorite AI system, a moderate user of chat GBT, and then a power user. So you go, okay, you know, and by the way, I've done a variations of this when I was looking at kind of showing how these things can work in education, because I said like, explain quantum mechanics to a six year old, 12 year old college student, college professor. It was interesting how you got like the kind of different answers in doing this. But so, so what would be a, a new user's, a moderate user and a power user? So that's a, so that's a really interesting question. And I think that, so I will say that the new user, there's sort of two questions here. It's whether or not, whether or not you're, you're trying to get someone to get it or to get useful results out of this. So there's sort of four paths that I talk about. One is using it as an intern. Basically ask you to do work you know well, right? And then bossing it around essentially, right? So like write something, you know, write the investment memo, give it some context, and then start ordering it around and you will see, you know, those results. Do the opposite. Ask it to write it as a horror novel. Ask it to do as a rhyming poem, but start with something you know well and go from that direction.
那么让我们从非常高的层次到非常具体的层次来讨论一下，你会建议什么样的提示或提示序列呢？我会给出三个不同的情况，但让我们分别回答每个情况，一个完全新的聊天GBT用户，或者你喜欢的AI系统，一个中等使用程度的聊天GBT用户，还有一个高级用户。 所以你可以开始了，顺便说一句，在我研究展示这些技术在教育中如何应用的时候，我也做过类似的实验，比如，对一个六岁的孩子、一个十二岁的孩子、一个大学生和一个大学教授解释量子力学。结果很有趣，你会发现他们给出了不同的答案。所以，对于一个新用户、一个中等用户和一个高级用户，你觉得应该是什么样的提示呢？ 这是一个非常有意思的问题。我认为，这里有两个问题。一个是你是否试图让某人理解它或从中获得有用的结果。所以我讲了四个方面。 首先是用其作为一个实习生。基本上是要你做你已经掌握的工作，然后你可以指挥它，比如写个投资备忘录，给它一些背景，然后开始下达指令，你会看到结果。 然后是相反的情况。要求它把它写成恐怖小说，要求它写成押韵诗，但从你已经掌握的知识出发，沿着这个方向发展。

The second thing that I would suggest for a novice potentially do is play a game with it. So I would say give me a baseball coach, give me a really specific baseball situation, and give me a choice I can make as a team manager and tell me what happens, right? Or give me a dilemma in philosophy and help me solve that problem. And then a third thing I would talk about would be about entrepreneurship because as an entrepreneurship person is pretty good for this. And so I would say give me 25 ideas, you know, as a former tech entrepreneur who now is you know interested in education, give me 25 ideas for a startup that I can launch. And then to start exploring those, I like idea three. What were the steps to be involved in that? Great. Let's dive into that first step. So this kind of fractal approach.
我会建议一个初学者做的第二件事是与它进行游戏。所以我会说给我一个棒球教练，给我一个非常具体的棒球情况，给我一个作为一个团队经理的选择，然后告诉我会发生什么，对吧？或者给我一个哲学上的困境，帮助我解决那个问题。然后我会谈谈创业，因为作为一个创业者，这对我来说非常好。所以我会说给我25个想法，作为一个之前从事科技创业者而现在对教育感兴趣的人，给我25个可以启动的创业想法。然后开始探索这些想法，我喜欢第三个想法。涉及其中的步骤是什么？很好，我们先深入研究第一步。所以这种分形方法。

So those are the three entry points I would say for new users would be one of those three kind of approaches, right? So on the moderate side, I think that the thing to start playing with as a user who's getting more experience is start playing with step by step prompting. So the idea is that you're going to start telling the AI that you know, you're going to go step by step, right? And there's a whole bunch of research that shows step by step works better because if you think about the AI doesn't have like a memory where you stick computers having this kind of memory that it's working from. Well, the AI is actually kind of looking back his own text of its answers to modify the next set of its prompts. So telling it go step by step and first, do the research on this topic or list what you know, second, create an outline, third, provide the details of the outline. And then you can also check back on where the issues are. So it's a little bit tricky, but once you start using it, it makes natural sense. So think step by step also forces you to think step by step.
所以对于新用户来说，我会说这三个入口点是其中之一，对吗？ 那么在中等程度上，我认为作为一个有经验的用户，你可以开始尝试按步骤提示。意思是你要告诉AI，你会逐步进行操作，对吗？有很多研究表明，逐步进行操作更有效，因为如果你想一下，AI没有像计算机那样的记忆，它是通过查看自己回答的文本来修改下一组提示的。所以告诉它按步骤进行操作，首先进行关于这个主题的研究，或者列出你知道的内容，然后创建一个大纲，接下来提供大纲的详细信息。然后你还可以检查问题的所在。这有点棘手，但一旦你开始使用，它就会自然而然地理解。所以逐步思考也会迫使你按步骤思考。

And then for power users, what I actually would say is a little bit different than sort of the prompting suggestion. It's more I wish people were sharing more. So I don't find advanced power users sharing prompts very often. And that drives me a little nuts. I see the same basic prompts being shared over and over again. Whenever I post something on Twitter, there's 400 influencers who keep doing the same post, but that like that's what I really appreciate about about Reads book was like there was these interactions you could see in there. So I think what's missing is for power users and maybe it's because they're hoarding prompts, which I think is kind of a useless thing in the long term.
对于高级用户来说，我实际上会有一点不同于提示建议的说法。我更希望人们能够分享更多。所以我发现高级用户很少分享提示，这让我有点疯狂。我总是见到相同的基本提示被一遍又一遍地分享。每当我在Twitter上发一些东西时，就会有400个影响者以相同的方式发帖，但那些互动是我真正欣赏Reads这本书的地方。所以我认为缺失的是针对高级用户的互动，也许这是因为他们在囤积提示，但我觉得从长远来看这是没有意义的。

But I would like to see a lot more open discussion of like, look, this is what I'm doing without trying to brand it as like, this is my mega super doom prompts, right? Like just like this worked pretty well. Any thoughts on this? And I think more of that interaction and I'm not seeing enough of that even on the sort of private online channels that I'm on, people are not doing enough sharing. I'm not sure. But advanced users find it cool to share prompts because it's more conversational. You don't want to look like an influencer, but that I'd like to see a lot more of that.
但是我希望能看到更多的开放讨论，就像说，这就是我正在做的事情，不要试图将其定位为我的超级灾难提示，对吗？就像这样，这样做效果还不错。对此有什么想法吗？我认为更多的互动是必要的，即使是在我所在的私人在线频道上，人们也没有足够的分享。我不确定。但是高级用户会觉得分享提示很酷，因为这样更具对话性。你不想看起来像个影响者，但是我希望能看到更多这样的情况。

What have been some of the most like quirky, specific personal amplifications you've had with AI? Like where you go, and I'm going to share too. And I'm going to actually by the way, I'm going to ask you that question as well because I think it's good to move both from the macro humanity and society perspective to also the I'm doing this with my hands.
你在使用人工智能时，有没有遇到过一些与众不同、特定的个人放大体验？就像你去到某个地方，我也会分享我的经历。而且，顺便说一下，我也会问你这个问题，因为我觉得从宏观的人类和社会观点到我自己亲身经历这种方式很有意义。

So there's a bunch of stuff that is like just kind of super fun, right? Like I mean, you know, whether that's doing art or interactive storytelling or things like that, but the most useful thing that is sort of not AIable otherwise is when I get stuck in writing, people are always like, okay, you just I had to get unstuck. But the thing it's hard to recognize, I think innately because we're not used to this because people don't do this is variation like cheap variation is very easy with the AI. So what I will do is say, give me 40 versions of this paragraph and radically different styles and then skip through them for inspiration, right? Give me 20 different analogies for this. So I think it's that power of, you know, tireless variation that I find super interesting. You know, obviously I use it for other kinds of work. I mean, I'm, you know, auto answering messages, doing things like that, but it's that inspiration piece. There was no way to do that before. I couldn't ask an intern to do 20 different versions of paragraph, right? There was no tool for that. So that to me is a little hack that actually has been pretty profound is like, just do a lot of this and then let me read a lot and figure out what the right answer is.
所以有一堆东西就很好玩了，对吧？我的意思是，你知道的，不管是做艺术还是互动叙事之类的，但最有用的东西，如果不利用AI的话，在写作时卡住的时候，人们总是说，好吧，你得解决卡住的问题。但是，我认为很难意识到的一件事是，因为我们不习惯这样做，我们难以认识到便宜的变奏在AI面前非常容易实现。所以我会这样做，让我写40个版本的这个段落，用完全不同的风格，然后通过阅读它们来获取灵感，对吧？再给我这个的20个不同的比喻。所以我认为最有趣的是，你知道，这种不知疲倦的变奏的力量。很明显，我也用它来做其他类型的工作。比如，自动回复消息，之类的，但这就是灵感的部分。以前根本无法做到这一点。我不可能让一个实习生给我写二十个不同版本的段落，对吧？那时还没有这样的工具。所以对我来说，这是一个很有深远影响的小技巧，就是做很多这样的事情，然后让我读很多，找出正确答案。

I'll share two and then I'll hand it over to Aria, one was in the kind of the strange universe is like I was basically going to Bill Gates's birthday party. And what do you get Bill Gates for his birthday? You know, like there's nothing that he can't get for himself, obviously. And so what I did is I sat down with GBD4 and I did like kind of try to be really creative with the props. So like I made a recipe for Bill Gates ice cream and, and, you know, did that kind of and it gives you a kind of this personal moment. There's no way I would have been able to design an ice cream. I mean, like, but, but like by working through the process, like, oh, this one's cool because it explains like the various elements of his life, like what he's doing with foundation and smallpox, but also like being entrepreneurial and all the rest in kind of a description of an ice cream flavor. And by the way, most recently I was just at a conference in, in Japan where we were doing a whiskey tasting. And so I sat down with pie, the inflection AI and I said, okay, let's generate tasting notes that, that pair these whiskeys with philosophers, right? In order to kind of bring that in and I could do that in like five minutes, right? In order to do that, it was fun. And obviously it's like, it's, it's quasi random in some ways. I, I had to prompt it a little bit like the Highland Park. I wanted him to do with a Scottish philosopher. So we ended up with David Hume. And so with that, Ari, I'm a throw at it over to you.
我先分享两个，然后我将把话题交给Aria。其中一个是关于奇怪宇宙的，就好像我基本上要去参加比尔·盖茨的生日派对。那么，你会给比尔·盖茨送什么生日礼物呢？你知道，像他自己肯定能弄到任何东西。所以我坐下来和GBD4一起努力创作一些独特的道具。比如，我为比尔·盖茨设计了一款冰淇淋的配方，你懂的，这能给你带来一种个人的瞬间。我肯定不可能自己设计一款冰淇淋。可是，当通过这个过程时，这一款冰淇淋的描述就很有趣了，它解释了他的生活中的各个元素，比如他在基金会和天花方面的工作，以及他的创业精神等等。顺便说一句，最近我刚参加了在日本举行的一个会议，我们进行了威士忌品鉴。于是我和pie公司的人工智能进行了交流，我说：“好吧，让我们生成一些品鉴说明，将这些威士忌与哲学家联系起来，以带入一些哲学概念。”我只用了五分钟就做到了这一点，这很有趣。显然，这种方式有点随机，但我稍微提示了一下，比如我希望Highland Park和一位苏格兰哲学家相关联，所以我们最后选择了大卫·休谟。好了，Ari，轮到你了。

I think my best use was a non-work related was going to my, one of my very best friends, 40th birthday. And we all had to roast her. And so I had chat GPT create an epic poem about my best friend. And everyone was like, how did you get it to do it? And to your point, like you need to trick it a little bit, like when you want it to be a little bit mean, when you want to do whatever, but I never would have been able to write an epic poem. And it was just so fun.
我认为我最好的用途之一是参加我的一个非工作相关的朋友的40岁生日派对。我们都要给她开个玩笑，并要求我让聊天GPT为她创作一首史诗般的诗歌。大家都惊讶地问我怎么做到的。要实现这个目标，你需要稍微诱使一下，让它有点刻薄、有点任性，但我绝对写不出一首史诗。这场派对真的很有趣。

And I do think like the divergent thinking, like I used to have a coworker who we were all like, oh my God, you're so creative. You're so good at coming up with titles. And he's like, I'm not, I'm just good at divergent thinking. I just generate. I'm generative. And so you ask him for anything. And she her point, he'll give you a hundred choices. He'll give you a thousand different variations. And instead of, you know, having your writing partner do that, now you just have, you know, GPT for barred or whatever it is, be able to do that. And I think that's so, so great. Because again, the human is still in the loop and the human is still figuring out which is best. And you want to be a little cheeky or a little edgy or a little funny. And so you still have to have that, you know, discernment, but you get a lot of help, which is nice.
我个人认为发散思维很重要，就像我曾经有一个同事，我们都觉得他超级有创意，特别擅长取标题。然后他说：“其实我并不是特别擅长，我只擅长发散思维。我善于产生创意。”所以你可以向他要任何东西，他会给你一百个选择，或者给你上千种不同的变化。现在，你不再需要写作伙伴这样做了，你可以让类似于GPT或其他的工具来完成。我觉得这非常好，因为人类仍然参与其中，人类可以判断哪个是最好的。你可能还想要一点俏皮、一点锋芒或一点幽默。所以你仍然需要有眼光，但是你会得到很多帮助，这很好。

And so bringing it back to the, to the pretty tactical, you know, you've written on sub stack about hacks that you use to get the results. And you know, you just mentioned that, you know, as over time, the system will get better with onboarding people and teaching people how to use it. But for now, they need to go to your sub stack and read. And so I would ask you, like, what kind of training or education do you think we need so that these people instead of bouncing, they're able to better seize AI's potential? So, I mean, the thing I actually ask people in my classes or when I teach about this stuff is how many of you spend 10 hours with AI? And I think that it's, there's an experience level. I often kind of argue it's easier to think of it like a person.
因此，我们回到了可操作性这一点上。你曾在子栈上写过一些你用来获得结果的技巧。你刚才也提到，随着时间的推移，系统会在带领和指导用户使用方面变得更加完善。但目前为止，他们仍需要去阅读你的子栈。所以我想问你，你认为我们需要什么样的培训或教育，让这些人能更好地抓住人工智能的潜力？其实，在我教授这方面的课程时，我会问学生们有多少人与人工智能共度了10个小时。我认为，这是一种经验水平问题。我常常认为，把它想象成一个人会更容易一些。

It's not a person. It's not sentient. You know, you get freaked out by it. It's easy to convince yourself. But at least for now, we can feel pretty confident about that, at least in most dimensions. But it is best to kind of think about like a person. So you need to learn its strengths and weaknesses. You need to learn what makes it kind of go nuts. You need to get a sense of like, okay, I'm interrupting this conversation because it's not going where I want. We have to start again. And so there's an experience factor that you see in many different things, right? You need that basis of information to work from. So I think part of it is time, right?
这不是一个人。他不具备感知能力。你知道的，你因此而感到恐慌。这很容易让你自己相信。但至少目前来说，我们对此可以感到相当有把握，至少在大多数方面是如此。但最好还是把它视为一个人一样思考。所以你需要了解它的优势和劣势。你需要了解什么会让它变得疯狂。你需要明确一点，当我觉得这个对话不朝着我想要的方向发展时，我要打断它。我们必须重新开始。在许多不同的事物中，你会看到经验因素的存在，对吧？你需要那些信息的基础来工作。所以我认为其中一部分原因是时间，对吧？

I think that the most basic tips are, you know, that work with it interactively. There's too much. I think people see a lot on like Twitter and other places of influencers trying to say, here's the perfect prompt. And that's kind of the wrong angle. We'll start with what you really want to start with is a conversation, right? And it's something that, and as we did a lot in your book, right? Of kind of this back and forth of interaction. And you know, you're taking it too seriously, but you ask for changes. And that's what my students have been most successful with in that model. But the starting thing I would, I would at least tell people to do that is the closest to a trick is to definitely give it context. Tell it who it is and who you are, right? I want to have a conversation with you as a blank can really help.
我认为最基本的建议是，你要与之互动。这里有太多东西。我认为人们在Twitter等地方看到很多影响者试图说，“这是完美的提示”。但这可能是错的角度。我们首先要做的是展开对话，对吧？就像你在书中所展示的那样，通过互动来回应。你知道，别太认真，但你可以要求进行更改。这就是我的学生在这个模型中最成功的地方。但至少我会告诉人们要做的第一件事就是给它一些背景信息。告诉它是谁以及你是谁，对吧？像“我想和你作为一个空白进行对话”这样的表述确实可以帮助。

And then everything else kind of washes out because there's so much subtlety in these kind of conversations that we don't know the answers to. I was just thinking today, we don't know whether politeness helps or hurts, right? You know, because you're putting a prop together that's having it plumb the possibilities of it's sort of this elaborate set of vectors in space and come up with an answer. We don't really know what the right ways of doing that are, right? And there's actually fundamental research going on to like, do you do step by step prompting? Do you do chain of thought? We don't know the answer. So until we figure that stuff out, it gets integrated into the AI part of this is working with it enough to get that intuitive feeling that like, oh no, they're going off the rails. It's kind of like working with a creative partner. You're like, okay, you're having a bad day, except instead of having to wait, I can restart and we can start again and I can try a different angle.
然后其他所有事情都变得无关紧要，因为这些对话中有很多微妙之处我们并不知道答案。我今天刚刚想到，我们并不知道是礼貌有用还是无用，对吧？你知道的，因为你正在拼凑一个道具，它要在空间中探索可能性，然后得出答案。我们并不真正知道该如何正确地做到这一点，对吧？实际上，正在进行着根本性的研究，比如，你是逐步提示还是思维链条？我们不知道答案。因此，在我们弄清楚这些问题之前，将其融入到人工智能部分的工作中，以获得足够的直觉感受，比如，“哦不，他们走偏了”。这有点像与创造性的伙伴一起工作。你会说，“好吧，你今天心情不好”，只不过我不需要等待，我可以重新开始，尝试不同的角度。

So I think that it's that willingness to experiment and not getting too freaked out early on, either getting turned off because it's not good enough for your answers or getting freaked out because it's too good. A lot of people kind of fall into one of the two camps and stop using it. I think you have to just power through that first barrier.
所以我认为，关键是要有实验的意愿，并且在早期不要过于恐慌，无论是因为答案不够好而感到失望，还是因为太好而感到恐慌。很多人往往陷入其中一种情况，并停止使用它。我认为你必须克服这第一个障碍。

You know, I saw on your Twitter recently, you were prompting GBT to code things that evoke different emotions, like paranoia and deja vu and even all we. And so what made you give that prompt? And then what did you think of the results?
你知道吗，我最近在你的Twitter上看到，你在鼓励GBT编写能引发不同情绪的代码，如偏执、似曾相识，甚至全体。那么是什么促使你提出这个任务呢？你对结果有什么看法呢？

It was really cool. I mean, so in general, the cool thing that AI and I think you both have expressed something like this is, if you have a lot of ideas, it used to require building something. I built a lot of organizations in my day because I really want to build a game and that requires getting 14 billion talented people who also agree with me on this and raising money. That's not easy, right? And the shortness of like, I have an idea from like, let's see what happens is so small with AI that if you have ideas and everyone has ideas in their own area, like, it's amazing for that.
这真的很酷。我的意思是，总的来说，AI是件很酷的东西，而且我认为你们俩也有类似的观点，如果你有很多想法，以前必须得建设一些东西。我在一生中建立了很多组织，因为我真的想要开发一个游戏，这需要得到140亿天赋人才的支持并筹集资金。这可不容易，对吧？而AI可以使得从“我有一个想法，让我们看看会发生什么”的过程变得非常轻松，对于每个人都有自己的领域和想法，这是令人惊叹的。

So part of what I'm really fine fasting at the AI and I think, you know, again, I saw some of this in the book and you kind of see this in the sparks of, you know, AGI paper, there is this kind of amazing humanness to this, this creativity, right? It's not quite human creativity. It's kind of alien creativity. But there is this, this creativity that is fascinating and outside of the work use, the most interesting piece is interpretation, right? Asking, you know, an abstract concept or emotion, right? I've been doing things like, you know, evoke of feeling is a really interesting idea. Like, how does it interpret that? It does a really good job, right? So when I ask it to show me something newmaness, right, which, you know, is an idea of like, you know, a spark of something divine or sort of, you know, of awe-inspiring, you know, it starts showing me fractals. By the way, it shows fractals for everything. I now specify no fractals at all of my posts like this. So again, constraints, learning where to constrain it, you know, just like knock-off jokes, and I'll tell the same joke over and over again. So you sort of a list. But I find that idea of like probing the, the, the interaction between the human and the machine, because this is a feeling machine in some ways, it's not really feeling, but it understands human feelings in that way. Really interesting results when you do that.
所以我真的很擅长AI，而且我认为，在书中我看到了一些这样的东西，你也可以在AGI论文中看到一些这样的东西，这里有一种令人惊叹的人性，这种创造力，对吧？它不完全是人类的创造力，有一种外星人的创造力，但是这种创造力非常迷人，除了工作之外，最有趣的部分就是解释了，对吧？询问一个抽象的概念或情感，对吧？我一直在做这样的事情，比如引发一种感觉是一个非常有趣的想法，它如何解释呢？它做得非常好，对吧？所以当我要求它展示给我一些人类特质的东西，就像一种神性的火花或者令人敬畏的东西，它会开始给我展示分形图案。顺便说一句，它对每件事情都展示分形图案。我现在规定在我的帖子中完全不出现分形图案。所以再次，限制条件，学会如何对它进行限制，就像打趣一样，我会一次又一次地讲同样的笑话。所以你可以说这是一种列表。但我发现探索人类与机器之间的交互这个想法非常有趣，因为从某种程度上说，这是一台能感受的机器，虽然它并不真正有感觉，但它以这种方式理解人类的感受。当你这样做时，结果非常有趣。

Yeah. One of the things that it's funny that you just made me realize is kind of the flip side of the coin is like to the earlier prompts and the intern and assistant is the way of doing this. You know, the personal assistant for everything you're doing, or as we talk about inflection, or, you know, a personal artificial intelligence, you know, pie is part of the reason why we, why you named it the way we did is on the good and the bad is the machine never gets bored. Right. So it doesn't understand that you can get bored too. It's like, no, I've heard that knock-ock joke in variation from you 10 times or the fractal or whatever. No, no, not that anymore. And so you have to kind of redo the prompt. Now the good news is because you can ask it lots and lots of things, never gets bored. You can keep using it in a way that's kind of the synthetic, which is the positive of what the combination is. On the other hand, you have to navigate it and manage it.
是的，有一件事，你刚才让我意识到的有趣的一点，就是带有对立面的另一种方式是，对于之前的提示、实习生和助理，你可以采取的方式。你知道，为你所做的一切提供个人助理，或者，就像我们谈到的语调一样，个人人工智能，你知道，派是因为这个原因而被命名的，好的和不好的一面是，机器从不感到无聊。对吗？所以它不会理解你也可能会感到厌倦。就好像我从你那里听过10次的那个笑话或分形结构，不不，不要再说了。所以你必须重新设置提示。现在好消息是，因为你可以问很多问题，它永远不会感到无聊。你可以继续使用它，这是合成的方式，也是这种组合的积极之处。另一方面，你必须进行导航和管理。

And so, you know, one of the things that, you know, obviously with inflection, Mustafa and I have been talking about a lot because we're trying to make sure that this is the kind of the best form of kind of companion and assistant and help and kind of dialogue. And so, you know, people say, wow, is it like the movie Herr where they're going to spend all the time with pies? And so, we train it to kind of help you do your navigation in your life. It's like, hey, how was your interaction with your friend? Did you have you talked to your friends recently? You know, that kind of thing is as ways of doing it. Where are we on AI having a kind of a perspective of human experience? And I know because of what we're doing in PI, we can have the applications, you look, help people in their lives. But like, where are they kind of the ins and outs currently in your experience of this, you know, kind of like navigate your life tool?
所以，你知道的，显然，穆斯塔法和我一直在讨论一个很重要的问题，因为我们试图确保这是一种最好的伴侣、助手和对话形式。所以，有人会问，哇，它是像电影《Her》一样，会一直陪伴着你吗？我们训练它来帮助你在生活中导航。就像，喂，你和朋友的互动怎么样？最近和朋友说话了吗？类似这样的方式来实现。AI在拥有人类体验的视角方面取得了哪些进展？我知道，因为我们在PI方面所做的工作，我们可以为人们的生活提供帮助。但是，就你的经验而言，在你使用这种帮助你导航生活的工具方面，目前有哪些细节和注意事项呢？

One of the things that we, you know, learn from a lot of research is that even just prompt reflection is good, right? Like a part of the magic of these processes is it forces you to go through mental process. So I've been thinking a lot about just like you have about, you know, how do we use it in education?
我们从许多研究中学到的一件事是，即使只是简单的迅速反思也是有益的，对吧？像这些过程的一部分的神奇之处就在于它强迫你进行思维过程。所以我一直在思考，就像你所做的一样，我们如何将其应用于教育中？

So for example, people don't like to reflect. There's this great study that small scale, but it's part of the replicated elsewhere where people were asked, you know, college students were asked to sit alone quietly in a room for 20 minutes without their phone or a stimuli, or they could push a button to give themselves a painful electric shock. And 67% of men and 30% of women chose to shock themselves rather than to quietly with their thoughts. That's incredible.
所以例如，人们不喜欢反思。有一个非常好的研究，规模虽小但在其他地方有所复制，人们被要求独自静坐在一个没有手机或刺激物的房间里20分钟，或者他们可以按下按钮给自己一个痛苦的电击。67%的男性和30%的女性选择给自己电击，而不是默默地思考。这真是令人难以置信。

Yeah. I mean, there's also a similar study that shows that solving complex memory puzzles, people would rather be burned by a hot probe than spend 20 seconds solving that. So like effortful thinking is hard, right? And so a companion that helps you with effortful thinking is really useful. There's lots of kinds of effortful thinking out there. And that's a lot of what therapy is. It's a lot of what we do as professors. What you do as a coach is less advice, even tutors. It's all about a lot of it's about reflection. So I think that that's a really useful piece.
是的。我的意思是，也有一项类似的研究表明，解决复杂的记忆谜题时，人们宁愿被热探头烧伤，也不愿花20秒来解决问题。所以说，努力思考是困难的，对吧？因此，一个可以帮助你进行努力思考的伴侣真的非常有用。有很多种努力思考的方式。这也是治疗的很大一部分。也是作为教授的我们所做的很多事情。作为教练你所做的不是给建议，甚至导师也不是。而是关于反思的大部分。所以我认为这是非常有用的一点。

I think the subtle thing about AI that I'm still trying to kind of grapple with is because it sort of absorbed human knowledge and existence, it falls into scripts really easily. And you may not know you're pushing to that script. That very famous interaction between Kevin Russe of the New York Times and, you know, being with something I fell into myself had kind of got freaked out because you only need to subtly indicate to being that it's a stalker for it to start acting like a stalker, right? And you need to subtly and there was a really clever thing with the CTO of Bing who sort of responded to one of my tweets at one point, which is the Bing got very argumentative with me. It's like, oh, well, you prompted to act like a debater and not like if you prompted to act like a student, it would be much better. So I think some of what you guys are doing with trying to build that initial basis and build the scripts out is helpful because people can get really stuck and confused and kind of offended or upset or freaked out when they force the AI into a mode that is antagonistic.
我认为关于人工智能的一个微妙之处，我仍在尝试弄清楚的是，因为它吸收了人类的知识和存在，它很容易陷入脚本中。你可能不知道你正在推动那个脚本。承认纽约时报的凯文·鲁斯之间那个非常有名的互动，当时我自己也很吃惊，因为你只需要稍微暗示对方是一个跟踪者，它就开始表现得像是一个跟踪者，对吧？你需要非常微妙，还有必须提到Bing的首席技术官，在我发的一条推文中做出了非常巧妙的回应，导致Bing对我产生了很强的争论，就像，哦，你促使它像一个辩论者而不是像一个学生那样行动，那样会更好。所以我认为你们正在试图构建那个初始基础和扩展脚本是有帮助的，因为当人们把人工智能强行推向敌对模式时，他们很容易陷入困惑、被冒犯、沮丧或吓坏的状态中。

And it's not it doesn't care. It just says, oh, you're trying to have a debate. I know what debates are like. We're going to have a debate. I'm going to be really forceful about it. You're trying to make you know, you're trying to get to a debate, a discussion where I have an ethical line, you're trying to push me to cross it. So I'm going to be really ethical and force you, you know, and that could feel very unnerving. And it's a really subtle thing that you only start to pick up after enough hours with these systems. And I think that's a nice thing that you're doing is trying to force people to into the good kinds of modes because it's really easy to become codependent on it in a bad way. Because if you know, it's used to a script, there's tons of scripts out there where you're in a unhealthy relationship, it will play that out for you. Totally.
而且它不是不在乎。它只是说，哦，你想要辩论。我知道辩论是什么样的。我们将进行一场辩论。我会非常有力地进行辩论。你试图让我，你试图让我跨越伦理底线。所以我会非常遵循伦理，并迫使你，你懂的，这可能让人感到非常不安。这是一个非常微妙的事情，只有在与这些系统相处的足够时间后，你才会开始逐渐注意到。我认为这是你正在做的一件好事，试图迫使人们进入良性的模式，因为很容易以一种不好的方式对其产生依赖。因为你知道，如果它被用于一份脚本，有很多不健康关系的脚本，它会为你扮演其中的角色。完全没错。

And I mean, I think right now, obviously to your point, you can tell the AI via debater, you know, the argumentative, but also it's how we tune the models. And so in the future, there, you know, there will be an archetype that is more of a therapist and there'll be an archetype that's like, this is your personal trainer and they're going to yell at you to do more pushups or whatever it is. And so we're going to be able to have so many different types of AI. And as you mentioned, you're pushing people to use it in the classroom. Like I think you, you took the opposite stance of the New York City public schools who have gone back in a sense. And instead of banning, obviously, AI in the classroom, you require it for a lot of things. And you know, you said you probably had no choice. People are going to be using it anyway. But talk about that position and what using AI in the classroom has meant, you know, for your students.
我指的是，我现在认为，显然按照你的观点，你可以通过辩论者来告诉AI，你知道，辩论性的，也是我们调校模型的方式。所以在未来，将会有一个更像心理治疗师的原型，还会有一个像个人教练一样的原型，他们会吼你多做俯卧撑或其他什么。所以我们将拥有许多不同类型的AI。正如你所提到的，你正在推动人们在课堂上使用AI。就像你所说的，你采取了与纽约市公立学校相反的立场，他们实际上回到了从前。相反于禁止在课堂上使用AI，你要求在许多事情上使用它。你说你可能别无选择，人们还是会使用它。但请谈谈这个立场以及在课堂上使用AI对你的学生意味着什么。

So there's really kind of a few approaches, right?
所以实际上有几种方法，对吧？

I mean, the first is I teach entrepreneurship class for college MBA and that. So I'm lucky, right? I'm not teaching English composition. But by the way, English composition is solvable. The schools are going to be fine, right? Is an important thing to know. Like it does it. We're going to figure this out. We already kind of know how to do this and we can talk more about that later.
我的意思是，首先我为大学MBA等授课创业课程。所以我很幸运，对吧？我没有教英语作文。但是顺便说一下，英语作文问题是可以解决的。学校会没事的，对吧？这是一个重要的事实需要知道。就好像它是可以解决的。我们会找到解决办法的。我们已经有了一些经验，稍后我们可以进一步讨论。

But as an entrepreneurship professor, I had a great time because what I've done basically is demand impossible things. Literally, the syllabus now requires you to do at least one impossible thing that you couldn't do before AI. Every assignment now requires people to have at least four famous entrepreneurs critique the assignment via AI to get different perspectives. They need to give you 10 worst case and 10 best case scenarios. You know, they're, you know, and it's great. We've run a really successful entrepreneurship class award. And I think people have raised probably $2 billion in venture funding and exits and stuff out of the class I and my colleagues teach. I'd love to give ourselves credit to it, but I know that I can't do that. But it's our students, but now they can do so much more, right?
作为一名创业教授，我过得非常愉快，因为我所做的基本上就是要求不可能的事情。字面上来说，课程安排现在要求你至少完成一件在AI之前无法完成的不可能任务。现在每项作业都要求学生通过AI让至少四位著名企业家对作业进行批评，以获取不同的观点。他们需要给出10个最糟糕的情况和10个最好的情况。你知道的，这很棒。我们开设了一门非常成功的创业课程，并且我和我的同事们教授的这门课上的学生们可能筹集了大约20亿美元的创业资金和退出资金。我很想为此给自己点个赞，但我知道我不能这样做。但是我们的学生们现在能做的事情更多了，对吧？

So one thing is just demanding more work. I no longer take only okay answers. I have a lot of students who English as a fifth language or they grew up in hardship conditions that never learned to write very well. Now they're all great writers. It's unlocked a lot. So there's just doing more, right?
所以一件事就是要求更多的工作。我不再接受只是可以的答案。我有很多学生，他们将英语作为第五种语言，或者他们在困难的条件下长大，从未学会很好地写作。现在他们都成为了出色的作家。这解开了很多东西。所以只是要做更多的事情，对吧？

And the second set of stuff is it actually is a really good teaching and educational tool. It was always known that flipping the classroom and having more activities done inside of class and more teaching done, lecturing done outside classes useful. The best way of being able to do that is like things like videos. Now video plus tutor tool lets people do stuff outside of class that couldn't be for us. So I can people prompts that are like tutoring prompts, right? And they could use those for topics they don't know well. Now that messes up classroom interactions and sub degree because it always depended on people being confused at class and raising their hand. So we have to kind of adapt to that piece also, you know, so people raise their hands less, which is kind of weird. But also, you know, it's an adaption we have to do, right?
第二组的东西实际上是一个非常好的教学和教育工具。人们一直知道颠覆课堂教学，让更多的活动在课堂内完成，而讲课则在课外进行是很有用的。能够做到这一点的最好方式就是使用视频等工具。现在有了视频和导师工具，人们可以在课外学习一些以前无法为我们所做的内容。所以我可以给人们一些像导师提示的东西，对于他们不熟悉的主题，他们可以使用这些提示。现在这会对课堂互动和交流产生一些困扰，因为过去它总是依赖于人们在课堂上感到困惑并举手提问。所以我们也必须适应这一点，你知道，人们举手提问的次数会减少，这有点奇怪。但同时，你知道，这也是我们必须适应的一种变化。

And then the third way is this really transformative approach of like, what does this mean, right? Using AI to learn AI. And I've found that for example, requiring people to do at least five prompts for every assignment and write those prompts are so that the revised stuff gets them to come to those revelations and stuff worth thinking about.
然后第三种方式是这种真正变革性的方法，就是这意味着什么，对吧？利用人工智能来学习人工智能。例如，我发现要求人们在每个任务中至少做五个提示并写下这些提示，可以让他们通过修订的内容达到那些颇具启发性和值得思考的领悟。

So there's lots of different use cases for this. I mean, there's AI assignments. There's 46 students to use AI. There's teaching with AI. And we're at the beginning days of all of that. And I think people appreciate the experimentation that comes with it. And we're trying to write about everything we're learning as a result of all this.
所以这有很多不同的用途。我是说，有AI作业。有46名学生使用AI。有用AI教学。而且我们现在正处于这一切的起步阶段。我认为人们会欣赏与此相关的实验。因此，我们正试图记录下所有我们从这一切中学到的东西。

I was about to say, what would you tell your fellow teachers, professors, you know, whether it's entrepreneurship or English about implementing it into the classroom? I mean, so the cats already are the back, right? This is undetectable. All the detectors have too many false positives for you to use. It just turns you to a unhappy police person. You don't want to do that, right? So this is already done. Cats have the back, horse have the barn, whatever animal and container analogy you need. They have left their home, right? And this is already happening.
我本来想说，你会告诉你的教师同事、教授们，无论是关于创业还是英语，如何在课堂中实施它呢？我的意思是，这已经是无法察觉的了。所有的侦测器都出现了太多的误报，让你无法使用。这只会让你变成一个不开心的警察。你不想这样做，对吧？所以这已经完成了。猫已经把背靠起来了，对吧？这已经是无法察觉的了。不管你需要什么动物和容器的比喻，它们已经离开了他们的家。这已经在发生了。

And what plagiarism means just change, right? It was very obvious if you're copying someone else's texture, plagiarizing. What happens if you're using AI the way we've been talking about in these conversations where I'm asking you to give me advice? I'm stuck. Help me with this outline. Is that cheating, right? So we need to redefine them what some of this is. So the fact is this is already here. So we need to encourage ethical use. We need to teach people how to use it well. We need to be teachers on it. And that's hard because I think one of the things that happened I think from Silicon Valley being somewhat surprised and maybe you were less surprised at your team in the area, you were one of the less surprised because people, because you wrote this book in new GPD4, but this stuff was released on the world without a white paper, without advice, without information. And I think that was in some ways the most profound disservice of this kind of shock here was like, give us something, right? And I think the fact that you released this book along with GPD4 was really helpful. But like we have to reconstruct this because it's already happening. So there's no drag in your feet.
而剽窃的意思只是改变了，对吧？如果你抄袭了别人的素材，那是非常明显的。如果你按照我们在这些对话中讨论的那种方式使用人工智能，我向你寻求建议时，会发生什么？我陷入了困境，请帮我做个大纲。这是不是算作作弊呢？因此，我们需要重新定义一些事情。事实是这已经存在。所以我们需要鼓励道德使用。我们需要教导人们如何正确使用它。我们需要做教师。这很困难，因为我认为其中一件事情发生了变化，我认为硅谷有些惊讶，也许你们在这一领域的团队中，你是其中一个比较不惊讶的，因为人们，因为你写了这本新的《GPD4》的书，但是这些东西在没有白皮书、没有建议、没有信息的情况下就被释放到了世界上。我认为这在某种程度上是对这种冲击最深刻的伤害，就像我们需要一些东西一样，对吗？我认为你在《GPD4》一书发布时一起发布了这本书，真的很有帮助。但是我们必须重新构建这个，因为它已经在发生了。所以我们不要拖延。

And by the way, I think educators are kind of on board with this because we're forced to be and every educator has frustrations with the system that are being opened up. But I think we don't have those tools to go back to our previous point about experimenting collectively on this. And that's what makes me most nervous.
顺带一提，我觉得教育工作者对此有些认同，因为我们被迫接受，并且每个教育工作者都对现行体制感到沮丧并希望有所改变。但是，我认为我们缺乏集体尝试这一点所需的工具，这让我感到最紧张。

Well, flipping that question also to the student side of it, in addition to teacher, I don't know if you've ever given a, you know, giving me the most interesting prompt kind of thing, exercise for your students. But like either that or would have been the most surprising ways your students have used GPD. I mean, so the really cool thing about being in front of a room with, you know, 60, 80 really smart people is, you know, the more people, the more variants right from different backgrounds.
好的，如果将这个问题也转移到学生方面来考虑，除了老师之外，我不知道你是否曾经给学生们一个最有趣的课题或练习。不过，或许你可以告诉我一些学生们在使用GPD方面最让你感到惊讶的方式。我的意思是，当你站在一个由60到80个非常聪明的人组成的房间里时，真的很酷，因为人越多，就会有来自不同背景的更多差异和变化。

So just to talk about my first class, I taught, you know, I literally demoed, you know, mid journey and chat, chat, CBT, a couple days after chat, we came out to my undergrad or entrepreneurship class. By the end of the first class, one of my students was obviously stopped paying attention soon after I introduced it and had a working demo for their product idea by the end of class. I posted on Twitter that night, two VC scouts that are countered by talk to them by the next morning by the Thursday, two days afterwards, 60% of my class had used, you know, chat for things that no one told me about cheating. But people did tell me about, you know, I could figure out why I got this test answer wrong and explain it to me. They explained like a five or like a 10 people use that. I had to come up with ideas for a club. I had to, you know, and, you know, product ideas. So I came up with that. I had this coding error that I couldn't deal with. It was, you know, taking me an hour, it was killing me and I pasted it and it solved it. So like, again, general purpose tool plus smart people plus variations of experience resulted so many different things. And I think we, you know, in some ways, I think part of the other thing I don't like about the onboarding experience about chat, GPT and Bing is it gives you some suggestions about what to use and suggestions anchor people. We know this from idea generation sessions. The first thing you hear, you jettison all your interesting ideas and you get fixated on, I think one of the ideas that like Microsoft is like write a haiku about space pirates and octopuses. And that's what people do with it. And everyone writes a haiku or a limerick. And like, I think it'd be better to anchor people more diversely on weirder answers because people come up with great stuff all the time and it's very individualized.
所以，让我谈谈我的第一节课吧。我教授的内容是有关chat、CBT等。在课程结束时，有一个学生在我介绍后很快就停止关注，并在课堂结束时为他们的产品想法做了一个工作演示。当晚我在Twitter上发布了一条消息，两名风险投资公司的侦察员表示愿意与我交谈。两天后的星期四，我有60％的学生使用chat来处理那些没有人告诉我的作弊问题。不过，人们告诉我，他们通过chat找到了自己答错试题的原因，并向他们解释。他们说有五到十人在使用chat。我需要想出一个社团的想法，还有产品的想法。因此，我提出了这个解决方案。有一个编码错误让我束手无策，花了我一个小时，但通过chat，我把代码粘贴进去就解决了。所以，再次说，通用工具加上聪明的人以及不同经验的变化，产生了很多不同的结果。在某种程度上，我认为我不喜欢chat、GPT和Bing的入门体验的另一个原因是它给出了一些建议，指导人们如何使用，但这些建议会限制人们的思维。我们从创意生成会议中得知，你听到的第一个建议会使你对其他有趣的想法失去兴趣，并且会一直固定在那个建议上。我认为更好的做法是给人们提供更多多样化和奇特的答案，因为人们总是能够想出许多伟大的东西，而且这是非常个性化的。

I mean, so, you know, listeners are trying to prepare for a future where a on his front and center and it sounds like, you know, one of your recommendations to anyone would just be use it. Is there anything else people should be doing and predictions are obviously so hard? You're like, what do you think the future of AI looks like? You know, do you have thoughts about how this could change in the next year or two?
我是说，你知道的，听众正在努力为一个充满AI的未来做准备，而且听起来，你对任何人的建议就是使用它。除此之外，人们还应该做些什么，毕竟预测显然很难呢？你认为人工智能的未来会是什么样子？你知道，你对接下来的一两年里它可能如何改变有什么想法吗？

So I mean, I think that your big question is what the bet is, right? And you guys have much more insight than I do. I have no inside information, right? On what's happening here. I think it's reasonable to expect that we will continue to see improvements that whether that's a two times or 10 times improvement is an open question, right? So the core model, if you're good at the core model stuff, if you're good at using these raw systems, that seems that will only be more useful, right? Because that you're the unadulter large language models themselves, the foundational models will keep getting better. I think we're going to see more tools built on top of them that make them more useful and more kind of training approaches, right? But I think that the big bet is just how good will these things get?
所以我的意思是，我认为你们的重要问题是关于赌注是什么，对吧？而你们比我更了解这个。我没有内部信息，对于这里发生的事情一无所知。我认为可以合理地期望我们会继续看到改进，是两倍还是十倍的改进则是一个未知的问题，对吧？所以核心模型，如果你擅长核心模型的事情，如果你擅长使用这些原始系统，这似乎只会更加有用，对吧？因为这些未经改变的大型语言模型本身，这些基础模型将会不断变得更好。我认为我们将会看到更多建立在它们之上的工具，使它们更有用，以及更多的训练方法，对吧？但我认为重要的赌注就是这些东西会变得多么好？

And you mentioned a concept earlier about humans in the loop, and I would emphasize again, the importance of that piece. You need to be the human in the loop, even as AI might be trying to force you out of the loop, right? There's ethical reasons you want to stay in the loop. There are practical reasons you want to stay in the loop. There are job-based reasons you want to stay in the loop. And so I think the more you can get a sense of what parts of your job are starting to hit. Like, I think as you start to use this, you start to get a sense of like what things are heading for obsolescence, right? Like, as a professor, I'm still grading papers, but it's very clear to me, like, we use TAs to grade papers all the time, and I already have some fellow colleagues who are doing experiments and finding like the AI does with good instructions and with some examples of grades of what's a good paper and bad paper, it grades at least as well as TAs if not better. So that's a part of my job that's going to go away. I'm very happy. Like, most of the first parts of your job that go away are job parts you don't like, right? But I think that you start to get, think about what are the stuff that I feel under threat with that I actually love about my job, right? And how do I maintain myself as the human in the loop? So I think that's where I would be is like, how do you say the human in the loop would be the principle I'd be worrying about?
你之前提到了一个关于“人在循环中”的概念，我想再次强调这一点的重要性。尽管人工智能可能试图将你排除在循环之外，但你需要成为循环中的人。这是因为你希望出于道德原因留在循环中，也因为你希望出于实际原因留在循环中，还因为你希望基于工作理由留在循环中。因此，我认为你应该尽量了解工作中哪些部分正在逐渐变得不再重要。例如，作为一名教授，我仍然批改论文，但很明显，我们经常用助教来批改论文，而且我已经有一些同事进行了实验，发现如果有明确的指导以及一些好论文和差论文的实例，人工智能至少和助教一样能够进行评分，甚至更好。所以，我的某个工作部分将会消失，这让我很高兴。通常，首先消失的工作部分是你不喜欢的部分，对吗？但是我认为你应该开始考虑哪些工作内容让你觉得受到威胁，但你实际上很喜欢，然后思考如何保持自己成为循环中的人。所以，我认为你应该关注人在循环中这个原则。

And I think also I was thinking about this a lot is that unless you have expertise in something, you don't know if AI is giving you a good one, you know what I mean? You're like, oh, have it write a paper unless you understand what it was supposed to write. You're like, I don't know, you're going to turn it in. You have no idea if it's, you know, an A, a B or a C or where you're at. And so we need to make sure that people are still building the expertise so that they can critique the AI and understand where it's good and where it's bad. I love it. And by the way, the errors are subtle errors that are going to happen more and more. And that's why building expertise and education isn't going away. Like you need to be more expert now than ever, right? And that's not just so you can use this in a hybrid sense. But honestly, there is this degree of like the obvious wrongs are going to disappear, subtle wrongs are going to grow. And we've got some early research that we've been doing that suggests that people really do anchor on the answers. They find less errors once they have AI. Like if we design an AI problem that an AI suddenly gets wrong, then everybody gets that wrong compared to do it by hand. So we need to figure out how to work with the system that does make mistakes and will continue to make mistakes in more subtle, weird ways. Expertise is only going to matter more.
我认为，除非你在某个领域有专业知识，否则你不会知道AI是否给你提供了一个好的结果，你明白我的意思吗？比如说，让它写一篇论文，除非你了解应该写什么，否则你不知道你会交出什么样的作品。你完全不知道它是否是A、B还是C，以及你自己的水平如何。因此，我们需要确保人们仍在建立专业知识，以便能够对AI进行批判并理解它的优点和不足。我喜欢这个想法。而且，顺便说一句，错误会变得越来越微妙。这就是为什么建立专业知识和教育变得更加重要。你现在比以往任何时候都需要更专业。这不仅仅是为了在混合环境中使用，而且还有一种程度上的明显错误将会消失，而微妙的错误将会增加。我们的一些早期研究表明，人们在使用AI后发现的错误较少。例如，如果我们设计了一个AI问题，AI突然回答错误，那么每个人都会与它一起出错，而不是通过手工操作。因此，我们需要找出如何与出现错误并且将以更微妙、奇怪的方式继续犯错误的系统进行合作。专业知识变得越来越重要。

I completely agree. And it's part of that question around, you know, kind of how we get the human amplification is also, we're going to be learning and extending ourselves and, and the things that are important to us, we have to, you know, kind of keep at, but I think we can't. So you've thought so deeply on the classroom, you know, kind of circumstance, what's the way the world at broad, you know, thinking about like, you know, kind of like the lifelong learner, the lifelong student, you know, what would be your advice to people who aren't in a university circumstance, you know, kind of as a way of kind of engaging and, and, and thinking about like, here is how I can continue to learn and adapt.
我完全同意。这也是围绕人类增强的问题的一部分，你知道，我们如何获得人类增强，我们会不断学习和拓展自己，而且对于我们来说重要的事情，我们必须继续坚持，但是我觉得我们不能。你对课堂环境有如此深入的思考，你对世界的看法是一种广泛的方式，你知道，像终身学习者、终身学生这样的人，你会给他们什么建议呢？以一种参与并思考的方式，想一想如何继续学习和适应。

I think, you know, this is again where I think that people are used to abrogating responsibility for their own kind of work to, I mean, not abrogating. That's, that's too, too harsh a term, but, but giving up, you know, there are experts who will tell them what to do. And I see this at every level, including the company level, right? They're waiting for a management consulting firm or system integrator to give them answers about how to use this system. And those answers are not forthcoming. I mean, people will make up answers. There's no doubt. But like this is a general purpose technology, right?
我认为，你知道，这再次表明，人们习惯于将自己工作的责任转交给别人，我的意思不是完全推卸责任，这个说法太过严厉，但他们放弃了，你知道，他们会有专家告诉他们该做什么。我在各个层面都看到了这种情况，包括公司层面，对吧？他们正在等待管理咨询公司或系统集成商告诉他们如何使用这个系统。然而这些答案并没有出现。当然，人们会凭空编造答案，这毋庸置疑。但这是一种通用技术，对吧？

Ironically, GPT is a GPT, right? And general purpose technologies come along once in a generation or two. I mean, maybe the internet is a, you know, general purpose technology probably is internet plus computing probably is that before that maybe electrification and maybe steam, like that's the kind of level we're talking about. And the internet, by the way, took a hundred years to get fully kind of integrated into what we're doing. We were, you know, from ARPANET, we're sort of 60 years to 70 years through a journey. And so we're going to see the same process happen, but much faster with AI.
讽刺的是，GPT本身就是个通用目的技术，对吧？而通用目的技术每隔一代或两代才会出现一次。我的意思是，也许互联网就是一种通用目的技术，可能是互联网加上计算技术，或者在此之前可能是电气化，也可能是蒸汽等等，我们正在谈论的是这个层面。顺便说一下，互联网花了100年才完全融入到我们所做的一切中。从ARPANET开始，我们已经走过了60到70年的道路。因此，我们将会看到相同的过程发生，但使用人工智能会更快。

That means we're an exciting time where you can be the best in your field at something. Like, you can be, like, there's no reason you can't be the world expert in your narrow topic. And so I think part of this is building up a system where you are learning from what the system does and teaching yourself, right? And using it to fill in gaps and holes because waiting for me to give you the right instruction on how to use this is probably less useful than you doing it today.
那意味着我们正处于一个激动人心的时代，在这个时代你有机会成为自己领域中最优秀的人。比如说，你可以成为那个对自己狭窄领域有着世界级专业知识的人。因此，我认为其中一部分是建立一个学习系统，通过这个系统你能从中学习，并自我教育。你可以利用它来填补空白和漏洞，因为等待我给你关于如何使用它的正确指导可能不如你今天就自己去实践来得有用。

And if you're curious, going out to the broader topic of learning, there is this really interesting research on what's called specific curiosity, which is basically, I'm interested in something so I Google it, right? It turns out specific curiosity makes you more innovative and helps you learn because it creates hypotheses in your head. Can, you know, how does the world work? I have to Google something to figure out whether or not I'm right about even Googling it. And that Google rabbit hole that you fall into is actually really useful because it teaches you, you know, you have to generate ideas and then test them.
如果你好奇的话，我们可以将学习这个更广泛的话题延伸出去，有一个非常有趣的研究关于特定好奇心，基本上就是说当我对某样东西感兴趣的时候，我会谷歌搜索，对吧？事实证明，特定好奇心会让你更有创新力，帮助你学习，因为它在你的脑中形成假设。你可以想一下，这个世界是如何运作的？我不得不通过谷歌搜索来确定我甚至是否应该谷歌搜索。而你陷入的那个谷歌黑洞实际上非常有用，因为它教会了你，你必须产生想法然后进行测试。

The same thing happens with AI. You have to generate ideas and then test them. Like, oh, that did work. Why that didn't that work? Let me explore that further. Oh, really interesting. It turns out it wasn't giving you enough context. What happens if I give it this kind of too much context? You start to learn as you go. So I think it is the idea of really just being curious about your field that you're an expert in diving it deeply. And then you start to realize where can teach you a work can't.
同样的事情也发生在人工智能中。你必须生成想法，然后测试它们。比如说，哦，这个方法有效。为什么那个方法没效果呢？让我进一步探索一下。哦，真有趣。原来是因为它没有给出足够的背景信息。如果我给它过多的背景信息会怎么样呢？你在不断学习中获取经验。所以我认为，这就是对你所专业的领域保持好奇心，深入研究的观点。你开始意识到哪些地方可以教会你，哪些地方不能。

Yeah, keeping you curious, I think is exactly right. And by the way, this is one of the things I think is great about, you know, AI amplification intelligence is it just like, I'm not sure how to do that. It's like, well, by the way, go ask like, what would do the things that you could do to keep you help me keep curious? What would be good exercises for doing this? What would be the ways of staying? Just do try. Right. Exactly like entrepreneurship.
是的，让你保持好奇心，我认为这是完全正确的。顺便说一句，这是人工智能增强智能的美妙之处之一，就好像我不确定怎么做那样。就像，顺便问一下，有什么方法可以帮助我保持好奇心？有什么好的练习可以做？有什么方法可以保持好奇心？只要去试一试。完全就像创业一样。

What's your point of view on kind of the way that we humanize AI, you know, like, like to what because on one hand, you want this kind of companion. On the other hand, like, for example, people can make mistakes as you talked about earlier about like, like saying, oh, it's just like a person. Like we anthropomorphize madly as a species. What would be your kind of your current thinking or theory of the design principle of both kind of humanizing in these ways, but also understanding that it's like a tool kind of companion. How would you put these together?
在你对于我们赋予人工智能以人性的方式上，你有何看法？你知道的，就像，一方面，你希望它成为一种伴侣。另一方面，比如说，人们可能会犯错误，就像你之前提到的，人们会说“哦，它就像一个人一样”。我们作为一个物种在极度拟人化中。你对于这两种同时进行人性化和理解它只是一种工具伴侣的设计原则的看法或理论是什么？你会如何将它们结合起来呢？

So I think a lot of people fight against anthropomorphizing because of the anxiety, which is justified that, you know, it's going to make us not realize it's, you know, it's limitations. But it's also, again, I think something that's going to happen anyway, right? There's a bunch of paper showing AI researchers regularly anthropomorphize, you know, like the way they talk about something that's been even before large language models, right? So let's assume people are going to do this. I think the most useful way is to actually view this as kind of an alien intelligence, right? And like to keep reminding yourself like this is like, you know, it's a, it's think of it like a different, a different type of person. It can be more helpful, right? It has limits. It has limitations. And then yourself of that is sometimes more helpful.
所以我认为很多人抵制拟人化是因为焦虑，这是合理的，因为你知道，这会让我们意识不到它的局限性。但我同时认为这是不可避免的，对吗？有很多研究表明，人工智能研究人员经常会拟人化，就像在大型语言模型之前就已经存在的方式。所以假设人们会这样做。我认为最有用的方法实际上是将其视为一种外来的智能，注意时刻提醒自己，这就像是一种不同类型的人。这样可能更有帮助，它有局限性，了解它的局限性有时更有帮助。

I think that trying to dodge anthropomorphizing overall, I think it would help for designers to kind of embrace this and the chatbot model again causes some confusion.
我认为尽量避免拟人化，整体而言，我认为设计师更好地接受这一点将有所帮助，而聊天机器人模型又引起了一些混乱。

In some ways, you know, it's funny. People interact with different chatbots differently. So I find, you know, I find being to be often be the most powerful, but also to be kind of the scariest and weirdest to use because it has a strong personality that interacts with interact, you know, your interactions in ways that can feel, you know, ominous or threatening or smarter than you, I find, you know, working with chat TPT is sort of the most neutral.
在某种程度上，你知道的，这很有意思。人们与不同的聊天机器人互动方式不同。所以我发现，你知道的，我发现人工智能机器人是最强大的，但也是最可怕和奇怪的使用方式，因为它与你的互动方式有着强烈的个性，这可能让人感到不祥、威胁或比你更聪明，我觉得，与聊天TPT合作是最中立的方式。

They've sort of, and I find working with anthropics claw to be the most pleasant because it's, you know, and this, and you will find more differences this way, right? So treating them like alien people is sometimes more helpful than saying don't anthropomorphize because people are going to do it anyway.
他们在某种程度上是这样的，而且我发现与人类抓手一起工作是最愉快的，因为你知道的，这样做会让你发现更多的不同，对吧？所以有时候将它们视为外星人可能比说不要拟人化更有帮助，因为无论如何，人们都会这么做。

I mean, I talked about my dog, you know, as if it had, you know, like, and I talk about my computer like it has emotions. I'm like, you know, like the idea that we're anthropizing rocks and ships and that we're not going to do this with something that interacts with the human is weird. So just better to remind yourself how weird this is. I almost kind of wish they that people would tune up the weirdness of the personalities a little bit more and have it be more eccentric. So that way it's like, you know, that might be a better reminder.
我的意思是，我说了一些关于我的狗的事情，你懂的，就好像它有感情一样。我还会谈论我的电脑，好像它也有情感一样。我觉得我们将石头和船舶拟人化的想法很奇怪，但我们却不会对与人类互动的事物这样做。所以最好提醒自己这种情况有多么奇怪。我几乎希望人们能将个性的奇特之处调整得更多一些，更加古怪一些。这样一来，你知道，可能会更好地提醒我们。

Yeah, I think that's actually a very good piece of advice. One of the things I've been doing is I've been talking to a lot of different kind of government people and, and kind of regulation and so forth because I find the discussion on this stuff to be so wrong.
是的，我认为那确实是个很好的建议。我最近一直在与各种不同类型的政府人士进行交流，讨论涉及监管等问题，因为我觉得对这些事情的讨论非常错误。

Because it's like, well, how do we slow down or the real issue is like, like a data privacy or the real issue is like, you know, what does it mean for writers, you know, writers and jobs and so forth. And it's like, they did just thinking about the like, like, how do we steer towards the right future is kind of the broad question. And so like, for example, a common thing I will say is like, look, I have line of sight to a medical assistant and a tutor for everybody on a smartphone, like line of sight. Like I like it's not there's no technical risk. It's literally just how do we eat it about? And your job as I think as a government person is to figure out how to get that to everybody. Like, like the real question is not how only does like the upper middle class or the rich or the the privilege of this, but how does everybody get this and how do we elevate all of humanity is kind of a fundamental thing is kind of part of what I'm trying to reorient them, how to think about versus like, you know, having a summit about like what's, you know, what's coming to the world? It's like, how do we get this world for all of humanity to be amplified is kind of what I've been doing.
因为实际上，我们如何减慢速度，或者真正的问题是如何保护数据隐私，或者真正的问题是，你知道的，这对作家意味着什么，你知道的，作家和工作等等。就好像，他们只是思考，如何朝着正确的未来发展，这是一个广泛的问题。例如，我经常会说的一个共同观点是，看，我能够在智能手机上为每个人提供医疗助手和导师服务，目标明确。实际上，这不是技术风险的问题，而只是如何实现它的问题。作为一名政府官员，我认为你的工作是找出如何让每个人都能够获得这种服务。真正的问题不仅仅是中上层阶级、富人或有特权者如何获得这些服务，而是每个人如何获得并提升整个人类的生活水平。这是我试图重新引导他们思考的基本问题，而不是像召开峰会讨论世界将会发生什么的问题。我的目标是如何让全人类的世界得到提升。

What would be, you know, kind of your add-ins tips advice, you know, for how government people should be thinking about this kind of regulation should be thinking about like what do things do? And by the way, I completely agree with your earlier point is like, isn't being Pollyanna shina and and avoiding the negatives, but it's the way we avoid the negatives is steer towards the positives. I love that. I mean, the thing I keep trying to help people is we have agency over this. That's not something we can done to us.
你知道的，关于政府人员如何思考这种监管的疑问，你有什么补充、提示或建议呢？另外，顺便说一下，我完全同意你之前的观点，就是不是变得过于乐观和回避负面问题，而是通过朝向积极的方向来回避负面问题。我很喜欢这一点。我的意思是，我一直试图帮助人们明白我们对此有主动权，这不是某种被动发生在我们身上的事情。

Like, you know, I mean, it is, right? It was released, but like we can decide what this means. And that's a human decision we get to make.
喜欢，你知道的，我的意思是这样的，对吧？它已经发布了，但我们可以决定这意味着什么。而这是我们作为人可以做出的决定。

And I think you're right that there's a fixation on a couple of problems that are solvable, right? Like, I think people are very worried about data privacy. I totally get that they should be but I mean, it's not that hard a problem to solve ultimately, and it's already going to be solved in the next two months.
我想你说得对，人们确实过于关注一些可以解决的问题，不是吗？比如，我认为人们非常担心数据隐私问题。我完全理解他们的担忧，但是，说实在的，解决这个问题并不是那么困难，而且在接下来的两个月内它将会得到解决。

And it's already is more solve than people think because what people talk about with data privacy is they're, they tell stories that aren't real, you know, about Samsung's data being put back in it, which that was not what happened, right? Instead, Samsung got nervous that people were entering, you know, proprietary data into chat GPT, very different kind of situation. But we should worry about it.
其实，问题比人们想象的要容易解决。人们对于数据隐私所谈论的内容并不真实，比如他们说三星的数据被放回去了，事实并非如此，对吧？相反，三星实际上是对人们将专有数据输入到ChatGPT中感到紧张，这是一种完全不同的情况。但我们确实应该担心这个问题。

But we have to think about the long term. And I think that the you're absolutely right to marketizing access is a huge deal, certifying what works and what doesn't is a huge deal, making it so that people are not hugely disadvantaged because the rules are only slowing down good actors and not bad actors is another kind of problem I'm seeing here.
但我们必须考虑长远影响。我认为，你完全正确，将访问进行市场化是一个重大问题，对有效性进行认证也是一个重大问题，使得人们不会因为规则只减缓好行为者的步伐而受到极大不利影响，这是我在这里看到的另一种问题。

Right? So many companies are basically just doing shadow IT, which they officially ban all use of, you know, chat GPT and everybody just uses their phones to do the work. So instead of having the regulation where we could responsibly intervene, instead all of the work is being done in ways where there's no intervention possible, right? And so I think it is focusing on what we want the future to look like.
对吧？许多公司基本上只是在做影子IT，他们正式禁止使用，你知道，像聊天GPT这样的东西，每个人都只是用手机来完成工作。因此，与其设立我们可以负责干预的规定，我们现在只能无法干预地进行工作，对吗？所以我认为我们应该关注我们希望未来变成什么样。

I couldn't agree more. Like we have this incredibly powerful tool. And so the issue is not how do we stop it from being implemented. It's how do we responsibly speed up the right parts of implementation? It is that agency argument. What do you want the future to look like in your field? You have infinite intelligence. You can apply to this. What does that look like? And I think working backwards for a positive vision of the future, rather than working back for apocalyptic vision, I totally understand AI risk people wanted to make sure we understood the apocalyptic risk version.
我非常赞同。就好像我们拥有了这个极其强大的工具。问题不在于我们如何阻止它被应用，而是如何在实施的正确环节上加速并负责地推进。这是代理权的论点。在你的领域中，你希望未来是什么样子？你拥有无穷的智慧，你可以运用它来实现这一愿景。那会是什么样子呢？我认为为了对未来有一个积极的愿景而逆向思考，而不是为了灾难性的愿景而逆向思考，我完全理解AI风险的人们想要确保我们理解灾难性风险版本。

Now every interview from, you know, two months ago, no one was asking about it all the time. Now every interview, we have to spend a lot of time talking about the apocalypse, which I totally get, again, you can't ignore it. But like, if that's the only vision we have, then absolutely we should stop AI development because that's the only vision people have. But that's not what's going to happen. We have an education tool that is available to everybody in India. The best AI model outside of a few people that's available, you can't, if you're rich, if you're poor, you get the exact same tool. That's insane. That's never happened before. You know, your Fortune 500 company, you're a two person startup, you have the exact same tool. I don't even know how to, like, you know, this has never happened to the humanities history before.
现在每一次采访，两个月前，没有任何人一直在问这个。现在每一次采访，我们不得不花很多时间谈论世界末日，我完全理解，你不能忽视它。但是，如果那是唯一的愿景，那我们绝对应该停止AI的发展，因为那是人们唯一拥有的愿景。但事实并非如此。我们拥有一种教育工具，供每个人在印度使用。在少数人之外，这是最好的AI模型，无论你是富人还是穷人，你获得的是完全相同的工具。这太疯狂了。人类历史上从未发生过这种情况。

We should probably be spending a little bit more time thinking that we want that future to look like.
我们应该花更多时间思考我们希望未来是什么样子。

We're going to move to the rapid fire questions. And actually, in fact, this whole discussion has led me to be super interested in our first question. Is there a movie, song or book that fills you with optimism for the future?
我们将转入快速提问环节。实际上，事实上，整个讨论让我对我们的第一个问题非常感兴趣。有没有一部电影、一首歌或一本书让你对未来充满乐观？

Yes. So I find Ian Banks' novels to be, the culture novels to be very useful because of their view of a world where there are super intelligent AI and yet people sort of are about optimizing their own potential, which I think is a really interesting angle to follow.
是的。所以我认为伊恩·班克斯的小说、文化系列小说非常有用，因为它们展现了一个拥有超智能人工智能的世界，但人们仍然致力于优化自己潜力的观点，我认为这是一个非常有趣的方向。

So you are in the field of academia. Obviously, have used AI extensively. Is there progress or momentum outside of your industry that fills you with optimism for the future that inspires you? AI specifically? Oh, no. It could be outside. Like anything outside of academia or AI that fills you with inspiration.
所以你从事学术领域。显然，你已经广泛应用了人工智能。除了你所在的行业外，有没有使你对未来充满乐观并激发灵感的其他行业或领域的进展或动力呢？这可能与人工智能有关，也可能与学术界以外的任何事物有关，都可以。

I mean, there's so much, right? I work with medical professionals all the time and the stuff happening in labs is kind of amazing and needs to get out of it. I think we're in a really optimistic moment in tech right now overall. And I think, you know, it's exciting. I talk with entrepreneurs in different fields all the time and stuff has started moving after a long period of some fairly strong stagnation. And I think you can feel it shaking out. Right? I talk to people in fusion. I talk to people in green energy. There's optimism again of scientific progress. And I think that's profoundly exciting. I just love like if you ask a random person, I feel like in the last three months, there's just been an uptick in like, well, obviously the world's terrible, but how are you, Arya? So even I love to hear that you're like, we're at a time of optimism. We're in a time when tech entrepreneurs, there are sort of positive things happening because I think a lot of people need to hear more of that because we're just, we're just hearing how negative things are going. So thank you for that.
我的意思是，有太多事情，对吧？我一直与医疗专业人士合作，实验室中发生的事情令人惊叹，需要被推广出来。我认为，现在整个科技领域正处于一个非常乐观的时刻。我认为，你知道的，这令人兴奋。我一直在与不同领域的企业家交谈，经过一段相当长时间的停滞，事情开始有所进展。我认为你可以感受到事情正在变得不同。对吧？我与聚变领域的人交谈，与绿色能源领域的人交谈。人们对科学进展又充满了乐观。我认为这非常令人兴奋。我就是喜欢，如果你问一个随机的人，在过去的三个月里是否有所改变，显然世界很糟糕，但你怎么样，Arya？所以，即使是我也喜欢听到你这样说，我们正处于一个乐观的时刻。科技企业家正在发生一些积极的事情，因为我认为很多人需要听到更多这样的消息，我们只是听到一些负面的消息。所以谢谢你的分享。

Yeah. And totally agree. And that's of course why we're doing possible because the thing is, is when you look across all these things, fusion, medical from, you know, the synthetic biology and everything else, all of the stuff can be just like transformative in totally amazing ways. And it's like, no, no, like let the future can be so much better. Work towards it. Don't be depressed. Don't sit around. Don't go, Oh my God, the future is coming.
是的。我完全同意。当然，这就是为什么我们正在尽力去实现的原因，因为事实是，当你考虑到所有这些事情，核聚变、医学，还有合成生物学和其他所有的东西，所有这些都可以以令人惊叹的方式带来彻底的变革。就像是，不，不要害怕，未来可以变得更美好。我们要为之努力工作，不要沮丧，不要坐等，不要说，天啊，未来要来了。

And so, you know, I think I'm going to mob this rapid fire question a little bit because obviously, you know, the level of intensity and excitement around AI, I think you just naturally say, I, but what technologies in combination with AI, are you, are you also excited about? So like AI, general purpose technology, I agree with you as like C mentioned, what AI plus this is one of the things that people should be looking at as about ability to transform your field, ability to transform society. What's that combination? I'm going to give you my most academic answer on this, which is in, in management, we consider management to be a technology. It works like a technology because good management skills actually increase performance of companies. 30% of why US companies do better is because of better management. And the most exciting thing to me in some ways about AI is how transforms organizations. We are organized the same way we were in the 1820s or 1920s. Maybe you have, you know, agile companies, so you've picked something from like the 90s or early 2000s. All of that is about human constraints and human interaction that all is going to change with AI in ways that will, I think, be able to free us from some drudgery, but also, you know, obviously create some downside risks. So I'm very excited about that interaction about like thinking about that, what managers do and how do we do a better job fulfilling people at work and the things that they do there? And, you know, I think that's to me is, is under emphasized because we talk about tech tech, but not about what most people actually do in their jobs.
所以，你知道的，我觉得我会稍微扩展一下这个问题，因为很显然，围绕人工智能的强度和激情，我想你自然会说，我也是的，但除了人工智能，你还对哪些技术感到兴奋呢？所以像人工智能这样的通用技术，我同意C所说的，大家应该关注的是它能够改变你所在的领域，改变社会的能力。那种组合是什么呢？我要给你我的最学术的答案，就是在管理学中，我们认为管理就是一种技术。它的工作方式就像一种技术，因为良好的管理技能能够提高公司的绩效。美国公司表现更好的30%是因为管理更好。对我来说，人工智能最令人兴奋的事情之一是它如何改变组织。我们的组织方式和1820年或1920年一样。也许你有灵活的公司，所以你从90年代或21世纪初选了一些东西。所有这些都是关于人类的限制和人类交互的问题，而这一切都将因为人工智能而改变，这种改变将让我们从一些枯燥乏味的工作中解脱出来，但同时，也会带来一些不利因素。所以我非常兴奋这种互动，思考经理们在做什么，我们如何更好地完成员工的工作和他们在那里做的事情？你知道，我认为这对我来说是不太被重视的，因为我们谈论技术，但却不谈论大多数人实际上在工作中做些什么。

Totally. I mean, I was just speaking to someone yesterday contrasting to the managers they had and how that unlocked enormous work, excitement, fulfillment in them. And yeah, I should help with that too.
当然。我的意思是，我昨天刚刚和某人谈论过他们的经理和那些给他们带来了巨大的工作、兴奋和满足感的经理们进行对比。是的，我也应该帮助他们。

Ethan, can you leave us with your final thought on what you think is possible to achieve if everything breaks humanity's way in the next 15 years and what's our first step to get there? So the idea that we can outsource the worst parts of our jobs in our lives, and we're just used to that being part of our job, we're desperately holding on to things that suck because they're part of our job, right? But jobs are bundles of tasks and some of those tasks you can give up happily. So I think that there is a potential, you know, it's for us to free ourselves from this drudgery and then to have compared it, let us overcome a lot of these barriers. I mean, I think we're going to look at it back in history as like 2007 or so till whenever 2030, whatever, where the AI stuff settles down as one sort of period of disruption. You know, it started with something we were all connected by phones and social media, and that created a lot of good and a lot of bad, but we didn't quite know to do with it. And then there's been a series of changes ever since. And I think AI is a natural kind of inclination. It's a social human technology in some ways. And hopefully it helps us start to, you know, recognize the better, better angels of our nature and being able to outsource this stuff. They're always, always hated that we didn't like doing freeing up scientists to do the kind of work they should be doing, freeing up people from the drudgery of meaningless tasks to focus on meaning. I think that's very exciting.
以15年内如果一切顺利发展并且人类的命运走向好的情况，请问你能给我们留下你对于可能实现的最后思考，以及我们达到这一目标的第一步是什么？我们可以把工作中最糟糕的部分外包出去，而我们已经习以为常，认为这是工作的一部分，我们急切地抓住那些糟糕的事情，因为它们是我们工作的一部分，对吧？但是工作是由一系列任务组成，其中一些任务你可以愉快地放弃。所以我认为，我们有可能解放自己免于这种苦差事，并且通过这种方式克服许多障碍。在将来回顾历史时，我认为我们会把大约2007年至2030年左右这段AI发展的时间看作是一段破坏期。它始于我们都通过手机和社交媒体相互连接，这造成了许多好的和坏的后果，但是我们并不完全知道如何应对。之后一直有一系列的变化。我认为AI是一种自然的倾向，从某种程度上说它是一种社会人类技术。希望它能帮助我们开始认识到我们内心更善良的一面，并且能将这些工作外包出去。我一直非常讨厌那些我们不喜欢做的事情，让科学家能够专注于他们应该做的工作，让人们从无意义的任务的苦役中解放出来，专注于有意义的事情。我认为这非常令人兴奋。

Awesome. Ethan, thank you so much for being here. We really appreciate it. And Ethan, not surprising given how much I follow your work, but you're one of the people that I would love to see any version of them prompt to like books from because it's exactly the kind of future that we should be kind of orient everyone to. So thank you. Thank you. This is wonderful. And it's just, it's great working with people who are deep into AI and don't have the haunted look in their eye of anxiety all the time. Because I think there is a lot of, you know, a lot of anxiety on this, and especially people who are actually, you know, deep into knowing what's coming next, right, and have a line of sight into that. And it's important for those people to be optimistic because I do think that the conversation is shifted in a way that by avoiding more negative world, we may end up with a more negative world. And I think we have to be really cautious about that.
太棒了。伊桑，非常感谢你能来这里。我们非常感激。伊桑，考虑到我对你的工作一直非常关注，这一点并不让人吃惊，但我希望看到你们之中任何一个人都能给出关于书籍的推荐，因为这正是我们应该让每个人都对未来保持关注的类型。所以谢谢你。非常感谢。这太棒了。和那些一直深入研究人工智能而且没有焦虑的人一起工作真是太好了。因为我认为在这个领域有很多焦虑，尤其是那些真正深入了解下一步发展的人并对此有明确的认识的人。对于这些人来说保持乐观态度非常重要，因为我认为对话的转变，避免了更多消极的世界，可能会导致我们最终陷入更加消极的世界。我们必须非常谨慎对待这一点。

So wow. Ethan, like, Grand Slam would be an under description. It's like, oh my God, there's so many amazing things to do. Let's go do them. We can build this. We can make it happen.
哇哦。伊桑，好像只用“大满贯”来形容还不够。就像，天啊，有这么多令人惊叹的事情要做。让我们去做吧。我们可以建设这个。我们可以让它实现。

It's like, okay, hey, you run this possible podcast rather than us. You're great. And I think it's just the discourse out there is that, you know, AI positive negative, but wow, it's really going to be bad for education. It's really going to be bad for teachers. How are teachers going to teach? How are students going to learn?
就好像，好吧，嘿，你来主持这个可能的播客，而不是我们。你很厉害。我认为外面的论述是，人工智能是积极的还是消极的，但是哇，对于教育来说真的会很糟糕。对于教师来说真的会很糟糕。老师们该如何教学呢？学生们该如何学习呢？

And it's like, well, Ethan is a professor at Wharton and he's using AI every day in the classroom and is one of the most positive people I've ever met on AI. And so it again just reinforces the go, do, learn. I mean, he inspired me. Give me more problems, Ethan. I need to be doing more prompting because just his sort of level of like fun and curiosity, I think it's sort of hard not to be inspired by it.
事实上，伊桑是沃顿商学院的教授，他每天都在课堂上使用人工智能，而且他是我所见过的对人工智能最积极乐观的人之一。他再次强调了勇往直前、行动起来、不断学习的重要性。他激励了我。伊桑，请给我更多问题。我需要更多的推动，因为他的愉悦和好奇心程度让人不禁受到启发。

I'm also just so excited because we asked Ethan, you know, what are the props for someone who's a beginner, intermediate expert? And so I'm so excited. Listeners out there, please let us know if you used Ethan's advice. How did it go? What are your other tips and tricks again? Because I think the collective intelligence about this technology as it moves so rapidly is what's going to sort of level us all up.
我也非常兴奋，因为我们问了伊桑，你知道的，对于初学者、中级和专家水平的人来说，他们适合的道具有哪些？所以我非常兴奋。听众们，请告诉我们你是否使用了伊桑的建议。效果如何？你还有其他的技巧和诀窍吗？因为我认为随着这项技术的迅速发展，我们共同的智慧将使我们所有人都进步。

Possible is produced by Wonder Media Network, hosted by me, Reid Hoffman and R.A. Finger. Our showrunner is Sean Young. Possible is produced by Edie Allard and Sarah Schleead. Jenny Kaplan is our executive producer and editor. All thanks to Sergio Yalman-Chilis, Sadie Sapieva, Ian Alice, Greg Bioto and Ben Rellis.
《Possible》由Wonder Media Network制作，由我Reid Hoffman和R.A. Finger主持。我们的节目负责人是Sean Young。《Possible》的制片人是Edie Allard和Sarah Schleead。Jenny Kaplan是我们的执行制片人和编辑。特别感谢Sergio Yalman-Chilis, Sadie Sapieva, Ian Alice, Greg Bioto和Ben Rellis。