智能是集体现象，而非人造物——Michael I. Jordan 教授（加州大学伯克利分校 / Inria）

Nature said that you are the most influential computer scientist. 《自然》说你是最具影响力的计算机科学家。 It exists in the real world. 它存在于现实世界中。 This is a abstraction, but it's a it's a real thing. 这是一种抽象，但它是真实存在的东西。 It's like f equals m a. 就像 F 等于 ma。 It's a set of it'll make predictions. 它是一套会做出预测的体系。 So if I write down a game, just like I wrote down f equals m a in some coordinate system, I can now predict what'll happen. 如果我写下一个博弈，就像我在某个坐标系中写下 F 等于 ma 一样，我现在就能预测会发生什么。 I don't think we need to see that. 我觉得没必要看到这些。 I think this anthropomorphizing of intelligence and understanding all that is not necessary, not appropriate, and is is a distraction for many many problems. 我认为，把智能和理解拟人化，这既没必要、也不恰当，而且对很多很多问题来说都是一种干扰。 Why say it? 为什么要这么说？ Understands. 理解。 I think it's science fiction, and I think science fiction is important for society, but it's also at the level it's being promoted and those kind of voices, it's really hurting 25 and 20 year olds. 我觉得那是科幻小说，科幻小说对社会很重要，但在这个层面上被如此推崇、被那种声音传播，真的在伤害二十几岁的年轻人。 These these young folks of whom there are huge numbers are excited about technology and they want to build things that help their family and help their country. 这些年轻人数量庞大，他们对技术充满热情，想做出帮助家人、帮助国家的东西。 Actually more of their family than their country, honestly. 说实话，更多是为了家人，而不是国家。 And they see real opportunities in doing that. 他们看到了真实的机会。 And they're kind of being told by the leaders, well, we had our fun. 然后那些领袖告诉他们，好，我们玩够了。 We developed a bunch of algorithms. 我们开发了一堆算法。 We did it and we were just interested in the pure understanding intelligence, even though they didn't understand intelligence. 我们做到了，我们当时只是对纯粹理解智能感兴趣，尽管他们其实也没弄懂智能。 They built gradient descent algorithms. 他们构建了梯度下降算法。 And now you guys, you can't do this because it's dangerous. 现在轮到你们了，但你们不能做这个，因为太危险了。 It's gonna it's gonna wipe out humanity with a with a high probability, or it's superintelligible to arrive soon, so there's nothing left to do. 它会以很高的概率消灭人类，或者超级智能很快就会到来，所以什么都不用做了。 That's in your lifetime. 就在你们这一生之内。 That is so demoralizing. 真的太打击士气了。 So demoralizing. 太打击士气了。 And that thing, I think that bothers me the most. 这件事，我觉得是最让我烦恼的。 I mean, the second part that bothers me is there's no economic thinking going on there. 第二个让我烦恼的，是那里面根本没有任何经济学思维。 So the current generation is just way too you know, there's not much thought going on, not much intellectual stuff. 这一代人就是，你知道，思考太少了，没多少真正的智识内容。 It's just, yeah, it's possible to build it, it's possible to steal the data from wherever you want to because that's what the Internet allowed to happen and not return any value to the person who originated the data. 就是，好，可以造出来，可以从任何地方偷数据，因为互联网允许这样做，也不需要向数据的原始创造者返还任何价值。 It's possible to run greedy descent on that but you need huge amounts of money but it's now possible to get it from people who aren't thinking very deeply. 可以对这些数据跑贪心下降，但需要海量资金，不过现在可以从想得不够深的人那里拿到钱了。 I I don't think it's bad to build systems you don't understand. 我不认为构建自己不理解的系统是坏事。 But I think this level of detachment from reality is unusual for human history. 但我觉得这种程度的脱离现实，在人类历史上是不寻常的。 This episode is supported by CyberFund. 本期节目由 CyberFund 赞助。 If you're building at the frontier of AI, they want to hear from you. 如果你在 AI 前沿领域构建产品，他们想听听你的想法。 CyberFund believes the future belongs to AI natives who want to achieve the impossible, and that is why they're introducing the monastery for AI native founders. CyberFund 相信未来属于想要实现不可能的 AI 原生者，这也是他们为 AI 原生创始人推出 Monastery 的原因。 It's an environment of pure focus and rapid execution for founders operating at AI native speed, 这是一个专注度极高、执行飞速的环境，专为以 AI 原生速度运转的创始人打造。 And they're offering teams $2,000,000 each to participate. 他们为每支参与团队提供 200 万美元。 Apply now at cyber.fund. 现在就去 cyber.fund 申请。 And what do you think about the term AGI, by the way? 顺便问一下，你怎么看 AGI 这个词？ AGI to me is just a bit of it's a it's a PR term. AGI 对我来说只是一个 PR 术语。 And it's some people think it's fun because you have to have these great aspirations. 有些人觉得很有意思，因为你得有宏大的抱负。 I think it's just distortionary. 我认为这只是在扭曲认知。 I think it confuses young people. 我认为它让年轻人感到困惑。 And as I will talk about today a little bit, I think that 1 of the things I find most alarming about the so called thought leaders that 1 will see often on podcasts and other venues is the alarmist tone or the exuberant tone. 而且正如我今天会稍微谈到的，我认为那些所谓的思想领袖，就是经常出现在播客和各种场合的那些人，最让我警觉的一点，就是他们的危言耸听或过度亢奋的语气。 And I think 20 and 25 year olds are watching that and saying, am I gonna be exuberant or am gonna be alarmist? 我觉得二十几岁的年轻人看到这些，会问自己：我是要亢奋，还是要危言耸听？ Those are the 2 choices. 就这两个选择。 And I hope that this conversation we're about to have is 1 that makes it clear to young people that there is other ways to approach life and technology. 我希望我们即将展开的这次对话，能让年轻人清楚地看到，面对生活和技术，还有其他的方式。 I've never actually thought of myself as an AI researcher. 我从来没有把自己想成一个 AI 研究者。 I didn't read an AI book. 我没有读过 AI 的书。 The term was coined in the fifties, and John McCarthy and others had particular goals in mind for coining it. 这个词是五十年代才创造出来的，约翰·麦卡锡等人创造它时，心里有特定的目标。 And they had particular methods in mind, like logical inference and so on, that didn't really quite pan out. 他们也有特定的方法，比如逻辑推断等等，但这些方法并没有真正取得成效。 In the meantime, in the sixties and seventies, you know, eighties, something arose called machine learning. 与此同时，六七十年代、到八十年代，出现了一个叫机器学习的东西。 The actual methods like decision trees and nearest neighbor and logistic regression and hidden 真正的方法，比如决策树、最近邻、逻辑回归和隐 Markov models were developed in other literatures, mostly statistics, operations research and so on. 马尔可夫模型，是在其他文献中发展起来的，主要是统计学、运筹学等领域。 And that led to industrial success stories. 这带来了工业界的成功案例。 So supply chains and commerce and transportation systems all used, and still to this day, vast amounts of machine learning. 供应链、商业和运输系统都用了，而且至今仍在用大量的机器学习。 They used gradient based methods and the cloud was developed to handle machine learning workloads at Amazon, in fact. 它们使用了基于梯度的方法，云计算实际上也是在亚马逊为了处理机器学习工作负载而发展起来的。 And so that's the tradition I came up in. 所以这就是我成长的传统。 I was trying to think about systems building at scale that would also serve multiple people. 我一直在思考如何构建大规模系统，同时也能服务多个用户。 The AI buzzword returned, I think, maybe 5 or so years ago because the the the data that got to be started to be used was language data. AI 这个流行词回来了，我觉得大概是五年前左右，因为开始被使用的数据变成了语言数据。 And so the box now is not just making predictions about supply chains or commerce or prices or whatever, it spits out human fluent language. 所以现在这个黑盒子不只是对供应链、商业、价格之类的东西做预测，它还能输出流畅的人类语言。 And people said, oh my god, we've solved the old AI problem, in fact, in some ways, if you define the AI problem narrowly like the Turing test, yeah. 人们说，天啊，我们解决了老 AI 问题，某种意义上，如果你用图灵测试这样狭义的定义来定义 AI 问题，那确实是的。 But there was this ongoing tradition of machine learning, and by that time, had incorporated people from all different kinds of 但还有这个延续中的机器学习传统，到那时候，已经吸纳了来自各种不同背景的人。 And it was really having an impact to the industry, still is. 它对工业界确实产生了影响，至今仍是。 But the AI buzzword returned because of LLMs. 但 AI 这个流行词随着 LLM 而回归。 And now to my view, it's been a distortionary effect on the path of research, on how we think about where research should go, but also on the path of how do we think about business models and how do we think about where technology is going. 在我看来，这对研究路径、我们思考研究方向的方式产生了扭曲效应，同时也扭曲了我们对商业模式和技术走向的思考方式。 And AI wasn't enough, they had to create this big hyped up buzzword, AGI, we will talk a lot about economics as a source of social intelligence. AI 还不够，他们得造一个更大的流行词：AGI，我们今天会大量谈到经济学作为社会智能来源的问题。 And when it's put together with machine learning style intelligence, you can now talk about at scale, not just numbers of computers and amount of data, but numbers of humans. 当它与机器学习式的智能结合起来，你就可以谈论规模化，不只是计算机数量和数据量，还有人的数量。 And that's critically important to me that role of humans as producers and consumers in these emerging systems should respected, amplified and thought about. 在这些新兴系统中，人类作为生产者和消费者的角色应该被尊重、放大，并加以思考，这对我来说至关重要。 Professor Michael Jordan, it's such an honor to have you on MLST, especially given that Nature said that you were the most influential computer scientist a little while back. Michael Jordan 教授，非常荣幸能邀请你来 MLST，尤其是《自然》不久前称你为最具影响力的计算机科学家。 It's funny because I was trained as a statistician and a cognitive scientist, but I'll take it. 有意思，我是以统计学家和认知科学家的身份接受训练的，但我接受这个说法。 Amazing stuff. 太厉害了。 Well, Michael, you've just published a paper called A Collectivist Economic Perspective on AI. 好，Michael，你刚刚发表了一篇名为《AI 的集体主义经济视角》的论文。 Give us the elevator pitch. 给我们说说核心观点。 I was never an AI person. 我从来不是一个 AI 圈子里的人。 So in some ways, it's easy for me to come in and look at people who are self professed AI researchers and sort of say, what are you doing? 所以在某种程度上，我很容易站在外面看那些自称 AI 研究者的人，然后问：你们在干什么？ What what is your what's your what's your point? 你的……你的目标是什么？ What's your goal? 你的目标是什么？ I think, sadly, they often don't have a very clear goal. 我觉得，遗憾的是，他们往往没有一个很清晰的目标。 It's it's that humans are intelligent. 就是，人类是智能的。 Humans are a computer. 人类是计算机。 The brain is a computer. 大脑是计算机。 And if we mimic that and take aspects of it and and, paralyze it and, power make it more powerful, 如果我们模仿它，取其精华，放大它，使它更强大， It'll just do great things. 它就会做出很棒的东西。 And and it kinda stops there. 然后就停在那里了。 It's not that there's a goal in, you know, society that we're gonna we're gonna try to do this or that. 并没有一个社会层面的目标说，我们要朝这个方向走还是那个方向走。 It'll just solve problems for us, and then we're we'll be happy. 它会帮我们解决问题，然后我们就会快乐。 And it it you know, I got away from Silicon Valley partly because that's just the way that people talk and I got tired of it. 我离开硅谷，部分原因就是那里的人就是这么说话，我受够了。 And there's not a lot of intellectual, know, let's call it deeper, long term thought going on. 那里没有太多深层的、长远的思考。 And now it became a rat race and a money race and all that. 现在变成了一场攀比和金钱的竞赛。 So so, yeah, my my perspective, I mean, it comes from a long tradition of other people having sort of social science perspectives on intelligence. 我的视角，来自一个很长的传统，有很多人从社会科学角度看待智能。 We are social animals, and a lot of our intelligence comes by the fact that we aggregate. 我们是社会动物，我们大量的智能来自于我们会聚合这一事实。 We aggregate opinions and thoughts, we have cultures and so on that retain them. 我们聚合观点和想法，形成文化将其保存下来。 Moreover, the society provides a context for our intelligence. 而且，社会为我们的智能提供了语境。 Smart action in 1 context is not in another context, and it's all very fleeting and contextual in the moment. 在某个语境中聪明的行为，放到另一个语境里就不是了，一切都非常短暂，完全在当下。 And so social science ideas are needed to appreciate what that means. 所以需要社会科学的思维框架才能理解这意味着什么。 When I say social science, I include economics, so game theoretic. 我说的社会科学包括经济学，也就是博弈论。 The context is somebody else out there is trying to take advantage of me or maybe to collaborate with me and I don't really know. 语境是这样的：外面有人想利用我，或者也许想跟我合作，而我并不知道是哪种。 And so I've got to put off feelers and do signals and create mechanisms where we can interact effectively and economics studies that in a mathematical way. 所以我得放出探针，发出信号，建立机制，让我们能有效互动，而经济学就是以数学方式研究这个问题的。 That attracts me because I am a mathematically inclined person. 这吸引了我，因为我是一个数学倾向很强的人。 I'm not a critiquer of AI. 我不是 AI 的批评者。 I want to make it right and I want to make it better and understand what it means to be intelligent in this world and safe interesting, think about long term issues. 我想让它做对，想让它更好，理解在这个世界上何为智能、何为安全有趣，思考长远问题。