[00:33:58] Warren Buffett: There are some problems that can’t be solved, and we shouldn’t be in the business of taking your money, investors’ money and tackling things that we don’t know the solution for. You can present the arguments, but it’s a political decision when you are dealing with states or the federal government. If you’re in something where you’re going to lose, the big thing to do is quit.
Warren Buffett:有些问题是无法解决的,而我们不应该拿着投资者的钱,去处理那些我们根本不知道解决方案的问题。你当然可以提出各种论证,但当事情涉及州政府或联邦政府时,最终就是一个政治决策。如果你处在一个注定会输的局面里,最重要的事情就是退出。
If you do present your case as well as you can, and everything else, but if you don’t hold the pen in the end, you know we don’t have any business taking your money and doing dumb things with us. We can do our best to explain what the intelligent things are, but it’s your money. So it’s very hard to tell on that. I have all the questions about politically determined decisions that are going to go to court in many cases, but you know that it doesn’t make sense. We know what we think a sensible system would be, and we ought to explain what we think it is and do our best to get our position because it’s pro-social. They have the right solution to have it. But the right solution, for example, in the Interstate, wasn’t to let 48 states each decide their own way of doing it, and award contractors the jobs. You know, there are some problems that can’t be solved, and we are not in the business of trying to solve insolvable problems.
即使你已经尽力把自己的立场讲清楚,但如果最终不掌握决定权,那么我们就没有理由拿着投资者的钱去做一些愚蠢的事情。我们当然可以尽力解释什么是理性的做法,但那毕竟是投资者的钱。因此在这种情况下,很难判断事情最终会如何发展。我经常遇到各种由政治决定的问题,很多情况下最终都会进入法院,但你其实也知道,这种方式并不合理。我们知道一个理性的体系应该是什么样,我们也应该把自己的看法解释清楚,并尽最大努力去争取我们的立场,因为那是对社会有益的。但真正正确的解决方案——比如当年的州际高速公路系统——并不是让48个州各自按照自己的方式去决定怎么做、各自去分配工程合同。确实有一些问题是无法解决的,而我们并不是一家去解决不可解决问题的公司。
But then the problem we have, of course, is that the people who work for you, that’s their job. So they want to have reasons to keep going, and those are tough choices if you’re managing, but that’s why they have managers.
但与此同时,我们也面临一个现实问题:那些为你工作的人,他们的职责就是把事情继续推进下去。所以他们总会找到理由继续做下去。而当你是管理者时,就必须面对这些艰难的选择,这也是为什么需要管理者存在的原因。
不受噪音干扰的情绪结构,想法如果不能收敛就会碎片化,听什么都有道理,分不清什么重要什么不重要;更不知道什么是重要并且可知的,什么是重要、不可知的信息。
*****
Becky Quick: This question comes from Scott Williams in Portland, Oregon. He said, “Do you think the net benefit of DOGE will be positive or negative for the long-term health of the United States?”
Becky Quick:这个问题来自俄勒冈州波特兰的 Scott Williams。他说:“你认为 DOGE 的净效应对美国的长期健康是正面还是负面?”
Warren Buffett: I think that bureaucracy is something that is amazingly prevalent and contagious even in our capital system, and big corporations overwhelmingly most of them look like they could be run better. I’m sure Berkshire does in many respects. And government is the ultimate. It really doesn’t have any checks on it.
Warren Buffett:我认为官僚主义在我们的资本体系中也惊人地普遍且具有传染性,绝大多数大型公司看起来都有提升经营的空间。我确信在很多方面 Berkshire 也是如此。至于政府,那更是终极形态——它实际上缺乏有效的制衡。
That’s why it scares you to some extent about what the future of the currency will be, because they can print currency. If you have people that get elected by promising people things – that doesn’t mean that they aren’t sincere about all kinds of items, but there’s no politician that says to anybody that has money, “I really think you have bad breath and if you don’t mind, would you step away from me.” It just doesn’t happen.
这也是为什么我们在一定程度上会对货币的未来感到担忧,因为他们可以印钞。若有人靠向民众许诺而当选——这并不意味着他们对各项主张不真诚,但没有政治人物会对有钱人说:“我觉得你口气很糟,麻烦离我远一点。”这种事不会发生。
I think the problem of how you control revenue and expenses in government is the one that is never fully solved and has really hurt dramatically many civilizations. I don’t think we’re immune from it, and we’ve come close to it.
我认为政府如何控制收支这个问题从未得到彻底解决,并且严重伤害过许多文明。我不认为我们能免疫,而且我们已多次接近危险边缘。
We’re operating at a fiscal deficit now that is unsustainable over a very long period of time. We don’t know whether that means two years or 20 years because there’s never been a country like the United States. But as Herbert Stein, the famous economist, said, “If something can’t go on forever, it will end.” We are doing something that is unsustainable, and it has the aspect to it that it gets uncontrollable to a certain point.
我们当前的财政赤字在很长时期内是不可持续的。究竟是两年还是二十年,我们并不知道,因为从未有过像美国这样的国家。但正如经济学家 Herbert Stein 所言:“不能永远持续的事,终将终止。”我们正在做一件不可持续的事,而且其中有一部分会在某个点变得不可控。
Paul Volcker kept that from happening in the United States, but we came close. We’ve come close multiple times. We’ve still had very substantial inflation in the United States, but it’s never been runaway yet. That’s not something you want to try and experiment with because it feeds on itself.
Paul Volcker 曾阻止这种情况在美国发生,但我们离临界点很近;而且不止一次。美国也经历过相当高的通胀,但尚未失控。你绝不想拿它做试验,因为一旦启动就会自我增强。
I wouldn’t want the job of trying to correct what’s going on in revenue and expenditures of the United States with roughly a 7% gap when probably a 3% gap is sustainable. The further away you get from that, the more you get to where the uncontrollable begins. It’s a job I don’t want, but it’s a job I think should be done. And Congress does not seem good at doing it.
我不愿意接手修正美国收支问题的工作——现在缺口大约是7%,而大概3%才是可持续水平。偏离越远,就越接近不可控。我不想做这件事,但我认为它必须有人去做。而国会看起来并不擅长此事。
We’ve got a lot of problems always as a country, but this is one we bring on ourselves. We have a revenue stream, a capital-producing stream, a brains-producing machine like the world has never seen. And if you picked a way to screw it up, it would involve the currency. That’s happened a lot of places.
作为一个国家,我们总会有很多问题,但这一项是我们自找的。我们拥有前所未见的收入创造力、资本生成力与人才生产机制。如果你非要挑一种方式把这一切搞砸,那就是货币问题。许多地方都发生过。
In theory, you would make it so there was substantial downside for anybody that screwed things up, but there isn’t downside. There’s upside. It’s the problem of the most successful company in the history of the country, in the history of the world.
理论上,你应当为任何把事情搞砸的人设置巨大的负面后果,但现实中并没有,反而有上行激励。这是这个国家、乃至世界历史上最成功体系所面临的问题。
Warren Buffett: We’re spending close – it’s hard to get the precise figure, but close to 20% of GDP on health. And if you go back to 1960, there were a number of countries that were each spending around 5%. And then the lines began to diverge dramatically. But the mathematical fact that there are only 100 percentage points in the equation didn’t change.
Warren Buffett:我们在医疗上的开支接近——确切数字很难给出,但接近——GDP 的 20%。如果回到 1960 年,当时有不少国家各自大约只花 5%。此后曲线开始显著分化。但“总量只有 100 个百分点”这一数学事实并没有改变。
So we tried that experiment with JP Morgan and Amazon and we had three people that didn’t think they knew the answer, but thought that in my case I use the term that it was a tapeworm in the economy. We also found out that the tapeworm was alive in every part of the country. I mean the hospitals liked it. The hospitals had prominent people working with people. People generally like their doctor, didn’t like the system. I mean all kinds of things, but in the end, JP Morgan and Amazon and Berkshire were not going to have any effect on changing that 20%.
所以我们与 JP Morgan 和 Amazon 一起做了那个尝试。我们三个人并不自认为知道答案,但我用过一个说法:这是经济体内的一条“tapeworm(绦虫)”。我们也发现,这条“绦虫”寄生在全国每个角落。比如医院也“喜欢”它,医院里有显赫人物在与各方打交道。人们普遍喜欢他们的医生,但不喜欢这个体系。情况五花八门,但最终,JP Morgan、Amazon 和 Berkshire 不可能把那 20% 改变掉。
Now that 20% – there are only 100 percentage points available and other countries spend six or 7% and perhaps use our system to their advantage which is also very true. That is an enormous percentage of an economy and we simply – it was too entrenched to really do much in the way of change.
而那 20%——总共也就 100 个百分点,别的国家花 6% 或 7%,还可能利用我们的体系为己所用,这也确实存在。医疗占经济的比重过于庞大,而这个体系根深蒂固,我们确实很难推动实质性改变。
And we spent some money on it and we did some work and we learned a good bit about our own systems and we saw the degree to which the present system was ingrained in so many people’s lives, whether the health care providers or whether everybody. And these aren’t evil people. I mean, they’re just going about something and trying to save lives.
我们花了些钱,也做了不少工作,从中学到了很多关于我们自身体系的东西,也看到了当下体系是如何深深嵌入无数人的生活——无论是医疗服务提供者,还是普通大众。这些人并不“邪恶”,他们只是各司其职、试图挽救生命。
But we found that whether it was in Canada or France or Britain or wherever it might be, that if you looked at our costs that they were just far higher and to some extent we were subsidizing the rest of the world. And people would come to the United States to do the really unusual or challenging aspects of health in terms of operations and that sort of thing.
但我们发现,无论在 Canada、France、Britain 或其他地方,相比之下我们的成本要高得多,而且在某种程度上我们是在补贴世界其他国家。很多人在涉及高难度手术等复杂医疗时会来到美国。
But we made no progress and there comes a point where the government – and I mean it’s so involved in the situation and health is so important to most to everybody, and we couldn’t – as I said to Jamie and Jeff, I said well the tapeworm won.
但我们没有取得进展,而到了某个节点,政府——我指的是政府在其中的深度参与——再加上医疗对几乎所有人都至关重要,我们也无能为力。正如我对 Jamie 和 Jeff 说的:“绦虫赢了。”
There are problems of society when you get 20% of your GDP going into a given industry, the degree of enthusiasm for changing that industry, the political power that the industry will have, and that doesn’t mean they’re evil. It’s just everybody – they just end up there.
当一个行业吃掉 GDP 的 20% 时,社会就会出现问题:你要改变该行业的热情会被削弱,该行业的政治力量会壮大。这不意味着他们“邪恶”,只是所有人最终都会被卷入到那样的格局里。
So I don’t know – we came to the conclusion we didn’t know the answer, the three of us, and we had the money to do it and we didn’t know how to change how 330 million people felt about their doctor, felt about our healthcare, what they felt entitled to.
所以我也不确定——我们三人得出的结论是:我们没有答案。我们有资金,但我们不知道如何改变 3.3 亿人对医生的感受、对医疗的看法以及他们认为自己“理应拥有”的权利。
It won’t change by itself and government is the only one that can change it and the only people in government that can change it are getting majority of 435 people and 100 people. And my dad lost one election in his life in 1948 and he was a very strong Republican and in 1950 he went back and beat the guy that beat him in 1948 and he got the doctors behind him. And they believe 100% in what they’re doing. They’re helping people every day.
这件事不会自发改变,只有政府才能改变;而政府中能改变它的人,必须在 435 人与 100 人中取得多数。我父亲一生只在 1948 年输过一次选举,他是铁杆共和党,1950 年他又回去击败了 1948 年击败他的对手,因为他争取到了医生群体的支持。他们 100% 相信自己所做的事——他们每天都在帮助他人。
And during the pandemic, the sacrifices made by people to save other people – just incredible. Can you imagine working in something where they’re bringing in people that are going to die by the dozens and dozens and dozens and you try to somehow keep your own morale up and keep working with them. So, you can’t argue about the importance of it.
在疫情期间,人们为拯救他人所作的牺牲——令人难以置信。想象一下,你在一个场景下工作,身边不断送来一批又一批濒危的人,而你要设法保持士气并继续投入。对医疗的重要性,这是无可争辩的。
But our costs are so different than any country in the world that it’s a huge element and we’re a very rich country. So we can do things other countries can’t do. And through our elected representatives and a whole variety of things over time, we’ve developed a system that is enormously resistant to any kind of major change and it’s important in every community that it’s in.
但我们的成本与世界任何国家相比都过于悬殊,而我们又是一个非常富裕的国家,因此我们能做其他国家做不到的事。长期以来,通过民选代表和各种过程,我们形成了一个对任何重大变革都极其“抗性”的体系,而且这套体系在每个社区都举足轻重。
我自己的看法更乐观一些,在人工智能的研究中,对“美、简洁、优雅(Beauty, simplicity, elegance)”的隐性偏好决定的泛化能力,某种程度上证明了好人(具备基本信任)能获得更强大的智慧,而坏人(缺乏基本信任)无论多精明,终其一生都只是一些支离破碎的小聪明。
So, I wish we had an answer for you, but I was somewhat pessimistic going in and I was a little more pessimistic when we came out. But I’m glad we did what we did and we learned something about our own failings in the process. So, Berkshire in effect got its money’s worth, but we didn’t kill the tapeworm.
所以,我也希望能给你一个答案,但进入时我就偏悲观,出来时更悲观一些。不过我仍庆幸我们做了那次尝试,也在过程中看见了自身的不足。换言之,Berkshire 的投入没有白花,但我们没能“杀死绦虫”。
Trying to change things in government is an interesting proposition in the country because you get self-selection in terms of the people that go into government and continue in it. And to some extent they keep having to make decisions that they don’t like as they go along and they learn to accept them or rationalize them or whatever it may be.
在这个国家,试图通过政府去改变,是一个“有趣”的命题,因为走进并留在政府的人群本身就具有自我筛选的特征。在某种程度上,他们一路上会做出自己并不喜欢的决定,并逐渐学会接受、或为之寻找理由,诸如此类。
But it’s still – this country’s worked out better than any country in the world. So you can’t argue it was a failure, but you can’t argue that there are certain problems that are terribly tough to figure out ways to solve. And of course, one of them gets back to the fiscal problem I mentioned before because it’s easy to spend money and it’s hard to cut people’s receipts.
但即便如此——这个国家的运转仍优于世界上任何国家。所以你不能称之为失败;但你也不能否认,有些问题极难找到解决路径。当然,其中之一又回到我之前提到的财政问题:花钱容易、削减他人收入很难。
And if you get elected, you’re going to say to yourself, well, I can do more good if I stay, and then if I really vote my conscience on this sort of thing. So, you give away a little bit here and a little bit there and a little bit there and finally you don’t recognize yourself in the mirror anymore. And that’s – I grew up in a political family, but I watched how people behaved and they behave like human beings which is what you have to expect and I behave like human beings.
而当你当选,你会告诉自己:如果我留在这里,我就能做更多好事;然后在这种事情上你若按良知投票……于是这里让一点,那里让一点,久而久之镜子里已认不出自己。我出身政治家庭,见过人们的行为模式,他们的表现与“人性”一致,这是你必须预期到的——而我也同样具有人的弱点。
We still manage to keep moving forward in dramatic way. It’s so much better to live here than it was 100 years ago or 200 years ago. It’s dramatic. So you can’t say the system’s a failure, but you can say that it is very difficult to make major changes in it.
我们仍然能以令人瞩目的方式向前推进。生活在今天远胜 100 年、200 年前,变化是剧烈的。因此不能说这套体系失败了,但可以说在其中推动重大变革极其困难。
田渊栋:更准确地说,它发生在 reasoning 或其他任务之下的“共同底层”机制,那就是 representation learning(表征学习)。
随着训练推进,模型的表征会不断演化。一开始更像是死记硬背;但随着足够的积累和联结,结构会突然“贯通”,从而出现类似“读书百遍,其义自见”的转折点。比如说在小学生的教育中,老师可能会先要求他们背诵一些知识,过段时间通过新的知识联结,原本模糊的含义逐渐显现,这就是顿悟的一部分。
课代表立正:也就是说,无论是 chain-of-thought 还是直觉判断,其实最终都依赖于“我如何表示、如何理解这个世界”这一底层机制?
田渊栋:对。比如,小学生可能解题靠穷举;而进入初高中后,引入了数学归纳法,仅靠简洁的证明就能覆盖无限情形,这种方法背后的“表示”就发生了根本性变化。神经网络的学习关键差异,也正体现在表征方式上。
“顿悟”描述了神经网络在训练过程中,性能从长时间的停滞(看似只会记忆),突然飞跃到能够完美泛化(真正理解了规律)的现象。这与人类学习中“读书百遍,其义自见”或武侠小说里张无忌先背下心法再融会贯通的体验惊人地相似。
那么,这个神秘的“突变”究竟是如何发生的?田博士用一个生动的“双峰模型”揭示了其内在的数学图景:
- 记忆与泛化的不同“解”:在一个复杂的优化空间中,“记忆”和“泛化”可以被看作两个不同的解,对应着两个不同的“山峰”。记忆是一种低效的解,需要模型记住所有特例;而泛化是一种高效、优雅的解,模型找到了数据背后更简洁的统一规律(short program)。
- 数据驱动的山峰演变:当训练数据不足时,“记忆山峰”更高,因为记住所有样本是降低训练误差最直接的方式。此时,模型的优化过程自然会收敛到这个山峰。
- 此消彼长的临界点:随着数据量的增加,数据中潜在的“泛化规律”开始显现。这使得“泛化山峰”逐渐升高,而“记忆山峰”相对降低。当数据量跨过一个临界点,“泛化山峰”的高度首次超过了“记忆山峰”。
- 顿悟的发生:由于优化算法总是倾向于寻找全局最优解(更高的山峰),在“泛化山峰”成为最高点的瞬间,模型的参数便会“雪崩式”地涌向这个新的、更优的解。宏观上,这就表现为一次突然的、性能飞跃式的“顿悟”。
这个解释极大地祛魅了“涌现”或“顿悟”的神秘感,将其从一个看似随机的魔法,还原为一个由数据分布和优化动力学共同决定的、有清晰路径的物理过程。泛化的能力并非凭空产生,它一直作为一种可能性存在于数据之中,等待着足够多的证据使其“脱颖而出”。这个比喻的深刻之处在于:
- 确定性:它告诉我们,“顿悟”不是随机的奇迹,而是当数据量达到某个临界点后,几乎必然会发生的相变。
- 竞争性:“记忆”和“泛化”是两种相互竞争的解决方案,模型在训练中会动态地选择在当前数据下“性价比”更高的那一个。
- 可操作性:它启发我们,促进“顿悟”的发生,关键在于如何设计数据和训练方法,来更快地“抬高”泛化山峰,“压低”记忆山峰。
Ilya Sutskever 01:25:13
Here is what I think is going to happen. Number one, let’s look at how things have gone so far with the AIs of the past. One company produced an advance and the other company scrambled and produced some similar things after some amount of time and they started to compete in the market and push the prices down. So I think from the market perspective, something similar will happen there as well.
我觉得会发生的事情大致是这样的。第一,我们先看看过去这几轮 AI 的演化是怎么走的:通常是某家公司率先做出一个重大突破,随后其他公司迅速跟进,在一段时间后搞出类似的东西,然后大家一起在市场上竞争,把价格往下打。我认为,从市场机制的角度看,这一轮也会出现类似的过程。
We are talking about the good world, by the way. What’s the good world? It’s where we have these powerful human-like learners that are also… By the way, maybe there’s another thing we haven’t discussed on the spec of the superintelligent AI that I think is worth considering. It’s that you make it narrow, it can be useful and narrow at the same time. You can have lots of narrow superintelligent AIs.
顺带一提,我们现在讨论的是“比较好的那条世界线”。什么叫“比较好的世界”?就是我们拥有这种强大的、类人学习者的 AI,同时还……再顺着说一个我们没怎么展开、但在设计 superintelligent AI 规格时很值得考虑的点:**你可以把它做窄一点,让它既超级智能,又在职能上保持窄域实用**。你完全可以有一堆“窄领域的超级智能 AI”。
But suppose you have many of them and you have some company that’s producing a lot of profits from it. Then you have another company that comes in and starts to compete. The way the competition is going to work is through specialization. Competition loves specialization. You see it in the market, you see it in evolution as well. You’re going to have lots of different niches and you’re going to have lots of different companies who are occupying different niches. In this world we might say one AI company is really quite a bit better at some area of really complicated economic activity and a different company is better at another area. And the third company is really good at litigation.
在这种设定下,假设有很多这样的系统,其中一家公司靠此赚取了巨额利润,然后另一家公司也杀进来开始竞争。竞争会怎么展开?答案是:**通过专业化来展开**。竞争最“偏爱”的就是分工与专业化——你在市场里能看到这一点,在进化里也能看到。最终你会出现大量细分“生态位”,对应一批占据不同生态位的公司。在那样的世界里,你可能会看到这样的格局:某一家 AI 公司在某种非常复杂的经济活动领域上明显更强;另一家公司则在另一个领域更胜一筹;还有第三家公司,也许在诉讼与法律业务上特别厉害。
Dwarkesh Patel 01:27:18
Isn’t this contradicted by what human-like learning implies? It’s that it can learn…
这难道不是和“类人学习能力”的含义相矛盾吗?因为它可以去学习……
Ilya Sutskever 01:27:21
It can, but you have accumulated learning. You have a big investment. You spent a lot of compute to become really, really good, really phenomenal at this thing. Someone else spent a huge amount of compute and a huge amount of experience to get really good at some other thing. You apply a lot of human learning to get there, but now you are at this high point where someone else would say, “Look, I don’t want to start learning what you’ve learned.”
它当然可以学,但这里还有“沉淀的学习成果”这件事。你已经在某个方向上投入了巨大的资源,你花了非常多的算力,才在这件事情上变得极其擅长、好到惊人。与此同时,另一个人(或系统)则在另一个方向上投入了同样巨量的算力和经验,在那件事上变得非常强。你们都用上了大量“类人学习”的能力才爬到各自的高点,而当你已经站在这个高度时,其他人就会说:“算了,我可不想从头开始学你已经学完的这些东西。”
自我强化带来两样东西,一是吸附力,被擅长的事牢牢吸住,越来越紧;二是越来越好的泛化能力,这是跨领域都适用的能力,最终的结果会是什么?
(1)算力资源有限
每家公司的“人脑 + GPU”都是稀缺的,最理性的做法是把自我强化的飞轮优先丢在“最能赚钱、回本最快”的那几块业务上。结果就是 Ilya 说的那种格局:一家公司在人类极复杂的某一块经济活动上远超同行,另一家在另外一块远超同行,第三家可能专门做诉讼这样的利基市场。竞争偏爱专业化,自我强化让专业化变成“惯性轨道”。
(2)算力资源无限
理论上,一家公司可以在每一个足够赚钱的岗位上都开一个独立的“类人学习体实例”,把整个经济里所有“正 NPV 的任务”全覆盖。这个世界里,约束从算力转向数据质量、监管约束和组织的管理复杂度——技术上可以全覆盖,现实里不见得有人能管理好这么庞杂的系统。
我自己有一个基本的判断是这个世界的分辨率会越来越高,分辨越高可以做的事越多,如果同样呈现幂律分布的规律,那么可以做的事、需要解决的问题会指数级的增长,算力资源永远都是不够的。
Dwarkesh Patel 01:27:48
I guess that would require many different companies to begin at the human-like continual learning agent at the same time so that they can start their different tree search in different branches. But if one company gets that agent first, or gets that learner first, it does then seem like… Well, if you just think about every single job in the economy, having an instance learning each one seems tractable for a company.
那这大概就要求很多不同的公司在差不多同一时间,都拿到这种“类人持续学习的智能体”,这样它们才能各自在不同分支上展开自己的那棵“搜索树”。但如果只有一家公司最先拿到了这种智能体,或者说最先拿到了这种学习者,那看起来就会变成这样……毕竟如果你只考虑经济体系里的每一种工作,让模型的不同实例分别去学习每一种工作,对一家公司来说似乎是可行的。
Ilya Sutskever 01:28:19
That’s a valid argument. My strong intuition is that it’s not how it’s going to go. The argument says it will go this way, but my strong intuition is that it will not go this way. In theory, there is no difference between theory and practice. In practice, there is. I think that’s going to be one of those.
这是一个有道理的论点。不过我的强烈直觉是:事情最后不会那样发展。你的推理指向那条路径,但我的直觉非常强烈地觉得,现实不会按那条路径走。从理论上讲,理论与实践之间没有差别;但在实践中,差别是存在的。我觉得这会是“理论和实践不一样”的又一个例子。
*****
You kind of ask yourself, is something fundamental or not fundamental? How things should be.
你会不断地问自己:什么东西是“基本的”、是“底层”的,什么不是?事物“应该是怎样的”?
I think that’s been guiding me a fair bit, thinking from multiple angles and looking for almost beauty, beauty and simplicity. Ugliness, there’s no room for ugliness. It’s beauty, simplicity, elegance, correct inspiration from the brain. All of those things need to be present at the same time. The more they are present, the more confident you can be in a top-down belief.
我觉得这些一直在很大程度上指导着我:从多个角度思考,同时去寻找某种“接近美感的东西”——美感、简洁性。丑陋的东西,是不该留下空间的。你要追求的是:美、简洁、优雅,以及来自大脑的“正确灵感”。这些要素需要同时出现,而且出现得越充分,你就越能在“自上而下的信念”(top-down belief)上有信心。
The top-down belief is the thing that sustains you when the experiments contradict you. Because if you trust the data all the time, well sometimes you can be doing the correct thing but there’s a bug. But you don’t know that there is a bug. How can you tell that there is a bug? How do you know if you should keep debugging or you conclude it’s the wrong direction? It’s the top-down. You can say things have to be this way. Something like this has to work, therefore we’ve got to keep going. That’s the top-down, and it’s based on this multifaceted beauty and inspiration by the brain.
这种“自上而下的信念”,就是当实验结果暂时和你唱反调时,支撑你继续往前走的东西。因为如果你永远只信任数据,那有时会出现这样一种情况:你做的是对的,但实验里有 bug,而你不知道那里有 bug。那你要如何判断,到底还要不要继续调试?是该说“这方向错了”,还是该说“系统里还有没找到的问题”?靠的就是这种 top-down 信念。你会对自己说:“事物必须是这样的,这种结构总得有一种方式是能工作的,所以我们得继续干下去。”这种 top-down 信念,正是建立在多维度的“美感”和“来自大脑的正确灵感”之上的。
头脑清晰是在大脑发展的早期就已经有一些简洁优雅的知识结构,在后面的人生中泛化到其他领域,必须是非常早的时期,两个方向(有安全感或者没有安全感)都是自我强化的,简洁优雅如果是胜出的一方只可能出现在非常早的时期,可能是1岁以前,甚至是娘胎里都有可能。
Appendix.31.Example.01.Charlie Munger
Warren Buffett: I’m Warren Buffett and that’s me with Charlie in an unscripted and spontaneous photo taken in Savannah, Georgia early in 1982. Our wives could tell us apart, as long as we wore name tags. When Charlie and I first met in 1959, if was as if twins who had been separated at birth had been reunited. But there were a few important differences between Charlie and me that people missed. Let me elaborate.